Source code for topostats.io

"""Functions for reading and writing data."""

from __future__ import annotations

import io
import logging
import os
import pickle as pkl
import re
import struct
from datetime import datetime
from importlib import resources
from pathlib import Path
from typing import Any

import h5py
import numpy as np
import numpy.typing as npt
import pandas as pd
import pySPM
import tifffile
from AFMReader import asd
from igor2 import binarywave
from ruamel.yaml import YAML, YAMLError

from topostats.logs.logs import LOGGER_NAME

LOGGER = logging.getLogger(LOGGER_NAME)


CONFIG_DOCUMENTATION_REFERENCE = """# For more information on configuration and how to use it:
# https://afm-spm.github.io/TopoStats/main/configuration.html\n"""

# pylint: disable=broad-except
# pylint: disable=too-many-lines


[docs] def read_yaml(filename: str | Path) -> dict: """ Read a YAML file. Parameters ---------- filename : Union[str, Path] YAML file to read. Returns ------- Dict Dictionary of the file. """ with Path(filename).open(encoding="utf-8") as f: try: yaml_file = YAML(typ="safe") return yaml_file.load(f) except YAMLError as exception: LOGGER.error(exception) return {}
[docs] def get_date_time() -> str: """ Get a date and time for adding to generated files or logging. Returns ------- str A string of the current date and time, formatted appropriately. """ return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
[docs] def write_yaml( config: dict, output_dir: str | Path, config_file: str = "config.yaml", header_message: str = None, ) -> None: """ Write a configuration (stored as a dictionary) to a YAML file. Parameters ---------- config : dict Configuration dictionary. output_dir : Union[str, Path] Path to save the dictionary to as a YAML file (it will be called 'config.yaml'). config_file : str Filename to write to. header_message : str String to write to the header message of the YAML file. """ # Save the configuration to output directory output_config = Path(output_dir) / config_file # Revert PosixPath items to string config = path_to_str(config) if header_message: header = f"# {header_message} : {get_date_time()}\n" + CONFIG_DOCUMENTATION_REFERENCE else: header = f"# Configuration from TopoStats run completed : {get_date_time()}\n" + CONFIG_DOCUMENTATION_REFERENCE output_config.write_text(header, encoding="utf-8") yaml = YAML(typ="safe") with output_config.open("a", encoding="utf-8") as f: try: yaml.dump(config, f) except YAMLError as exception: LOGGER.error(exception)
[docs] def write_config_with_comments(args=None) -> None: """ Write a sample configuration with in-line comments. This function is not designed to be used interactively but can be, just call it without any arguments and it will write a configuration to './config.yaml'. Parameters ---------- args : Namespace A Namespace object parsed from argparse with values for 'filename'. """ filename = "config" if args.filename is None else args.filename output_dir = Path("./") if args.output_dir is None else Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) logger_msg = "A sample configuration has been written to" # If no config or default is requested we load the default_config.yaml if args.config is None or args.config == "default": config = (resources.files(__package__) / "default_config.yaml").read_text() elif args.config == "topostats.mplstyle": config = (resources.files(__package__) / "topostats.mplstyle").read_text() logger_msg = "A sample matplotlibrc parameters file has been written to" # Otherwise we have scope for loading different configs based on the argument, add future dictionaries to # topostats/<sample_type>_config.yaml else: try: config = (resources.files(__package__) / f"{args.config}_config.yaml").read_text() except FileNotFoundError as e: raise UserWarning(f"There is no configuration for samples of type : {args.config}") from e if ".yaml" not in str(filename) and ".yml" not in str(filename) and ".mplstyle" not in str(filename): create_config_path = output_dir / f"{filename}.yaml" else: create_config_path = output_dir / filename with create_config_path.open("w", encoding="utf-8") as f: f.write(f"# Config file generated {get_date_time()}\n") f.write(f"# {CONFIG_DOCUMENTATION_REFERENCE}") f.write(config) LOGGER.info(f"{logger_msg} : {str(create_config_path)}") LOGGER.info(CONFIG_DOCUMENTATION_REFERENCE)
[docs] def save_array(array: npt.NDArray, outpath: Path, filename: str, array_type: str) -> None: """ Save a Numpy array to disk. Parameters ---------- array : npt.NDArray Numpy array to be saved. outpath : Path Location array should be saved. filename : str Filename of the current image from which the array is derived. array_type : str Short string describing the array type e.g. z_threshold. Ideally should not have periods or spaces in (use underscores '_' instead). """ np.save(outpath / f"{filename}_{array_type}.npy", array) LOGGER.info(f"[{filename}] Numpy array saved to : {outpath}/{filename}_{array_type}.npy")
[docs] def load_array(array_path: str | Path) -> npt.NDArray: """ Load a Numpy array from file. Should have been saved using save_array() or numpy.save(). Parameters ---------- array_path : Union[str, Path] Path to the Numpy array on disk. Returns ------- npt.NDArray Returns the loaded Numpy array. """ try: return np.load(Path(array_path)) except FileNotFoundError as e: raise e
[docs] def path_to_str(config: dict) -> dict: """ Recursively traverse a dictionary and convert any Path() objects to strings for writing to YAML. Parameters ---------- config : dict Dictionary to be converted. Returns ------- Dict: The same dictionary with any Path() objects converted to string. """ for key, value in config.items(): if isinstance(value, dict): path_to_str(value) elif isinstance(value, Path): config[key] = str(value) return config
[docs] def get_out_path(image_path: str | Path = None, base_dir: str | Path = None, output_dir: str | Path = None) -> Path: """ Add the image path relative to the base directory to the output directory. Parameters ---------- image_path : Path The path of the current image. base_dir : Path Directory to recursively search for files. output_dir : Path The output directory specified in the configuration file. Returns ------- Path The output path that mirrors the input path structure. """ # If image_path is relative and doesn't include base_dir then a ValueError is raised, in which # case we just want to append the image_path to the output_dir try: # Remove the filename if there is a suffix, not always the case as # get_out_path is called from save_folder_grainstats() if image_path.suffix: return output_dir / image_path.relative_to(base_dir).parent / image_path.stem return output_dir / image_path.relative_to(base_dir) except ValueError: if image_path.suffix: return output_dir / image_path.parent / image_path.stem return Path(str(output_dir) + "/" + str(image_path)) # AttributeError is raised if image_path is a string (since it isn't a Path() object with a .suffix) except AttributeError: LOGGER.error("A string form of a Path has been passed to 'get_out_path()' for image_path") raise
[docs] def find_files(base_dir: str | Path = None, file_ext: str = ".spm") -> list: """ Recursively scan the specified directory for images with the given file extension. Parameters ---------- base_dir : Union[str, Path] Directory to recursively search for files, if not specified the current directory is scanned. file_ext : str File extension to search for. Returns ------- List List of files found with the extension in the given directory. """ base_dir = Path("./") if base_dir is None else Path(base_dir) return list(base_dir.glob("**/*" + file_ext))
[docs] def save_folder_grainstats(output_dir: str | Path, base_dir: str | Path, all_stats_df: pd.DataFrame) -> None: """ Save a data frame of grain and tracing statistics at the folder level. Parameters ---------- output_dir : Union[str, Path] Path of the output directory head. base_dir : Union[str, Path] Path of the base directory where files were found. all_stats_df : pd.DataFrame The dataframe containing all sample statistics run. Returns ------- None This only saves the dataframes and does not retain them. """ dirs = set(all_stats_df["basename"].values) LOGGER.debug(f"Statistics :\n{all_stats_df}") for _dir in dirs: LOGGER.debug(f"Statistics ({_dir}) :\n{all_stats_df}") try: out_path = get_out_path(Path(_dir), base_dir, output_dir) # Ensure "processed" directory exists at the stem of out_path, creating if needed if out_path.stem != "processed": out_path_processed = out_path / "processed" out_path_processed.mkdir(parents=True, exist_ok=True) all_stats_df[all_stats_df["basename"] == _dir].to_csv( out_path / "processed" / "folder_grainstats.csv", index=True ) LOGGER.info(f"Folder-wise statistics saved to: {str(out_path)}/folder_grainstats.csv") except TypeError: LOGGER.info(f"No folder-wise statistics for directory {_dir}, no grains detected in any images.")
[docs] def read_null_terminated_string(open_file: io.TextIOWrapper, encoding: str = "utf-8") -> str: """ Read an open file from the current position in the open binary file, until the next null value. Parameters ---------- open_file : io.TextIOWrapper An open file object. encoding : str Encoding to use when decoding the bytes. Returns ------- str String of the ASCII decoded bytes before the next null byte. Examples -------- >>> with open("test.txt", "rb") as f: ... print(read_null_terminated_string(f), encoding="utf-8") """ byte = open_file.read(1) value = b"" while byte != b"\x00": value += byte byte = open_file.read(1) # Sometimes encodings cannot decode a byte that is not defined in the encoding. # Try 'latin1' in this case as it is able to handle symbols such as micro (µ). try: return str(value.decode(encoding=encoding)) except UnicodeDecodeError as e: if "codec can't decode byte" in str(e): bad_byte = str(e).split("byte ")[1].split(":")[0] LOGGER.debug( f"Decoding error while reading null terminated string. Encoding {encoding} encountered" f" a byte that could not be decoded: {bad_byte}. Trying 'latin1' encoding." ) return str(value.decode(encoding="latin1")) raise e
[docs] def read_u32i(open_file: io.TextIOWrapper) -> str: """ Read an unsigned 32 bit integer from an open binary file (in little-endian form). Parameters ---------- open_file : io.TextIOWrapper An open file object. Returns ------- int Python integer type cast from the unsigned 32 bit integer. """ return int(struct.unpack("<i", open_file.read(4))[0])
[docs] def read_64d(open_file: io.TextIOWrapper) -> str: """ Read a 64-bit double from an open binary file. Parameters ---------- open_file : io.TextIOWrapper An open file object. Returns ------- float Python float type cast from the double. """ return float(struct.unpack("d", open_file.read(8))[0])
[docs] def read_char(open_file: io.TextIOWrapper) -> str: """ Read a character from an open binary file. Parameters ---------- open_file : io.TextIOWrapper An open file object. Returns ------- str A string type cast from the decoded character. """ return open_file.read(1).decode("ascii")
[docs] def read_gwy_component_dtype(open_file: io.TextIOWrapper) -> str: """ Read the data type of a `.gwy` file component. Possible data types are as follows: - 'b': boolean - 'c': character - 'i': 32-bit integer - 'q': 64-bit integer - 'd': double - 's': string - 'o': `.gwy` format object Capitalised versions of some of these data types represent arrays of values of that data type. Arrays are stored as an unsigned 32 bit integer, describing the size of the array, followed by the unseparated array values: - 'C': array of characters - 'I': array of 32-bit integers - 'Q': array of 64-bit integers - 'D': array of doubles - 'S': array of strings - 'O': array of objects. Parameters ---------- open_file : io.TextIOWrapper An open file object. Returns ------- str Python string (one character long) of the data type of the component's value. """ return open_file.read(1).decode("ascii")
[docs] def get_relative_paths(paths: list[Path]) -> list[str]: """ Extract a list of relative paths, removing the common suffix. From a list of paths, create a list where each path is relative to all path's closest common parent. For example, ['a/b/c', 'a/b/d', 'a/b/e/f'] would return ['c', 'd', 'e/f']. Parameters ---------- paths : list List of string or pathlib paths. Returns ------- list List of string paths, relative to the common parent. """ # Ensure paths are all pathlib paths, and not strings paths = [Path(path) for path in paths] # If the paths list consists of all the same path, then the relative path will # be '.', which we don't want. we want the relative path to be the full path probably. # len(set(my_list)) == 1 determines if all the elements in a list are the same. if len(set(paths)) == 1: return [str(path.as_posix()) for path in paths] deepest_common_path = os.path.commonpath(paths) # Have to convert to strings else the dataframe values will be slightly different # to what is expected. return [str(path.relative_to(deepest_common_path).as_posix()) for path in paths]
[docs] def convert_basename_to_relative_paths(df: pd.DataFrame): """ Convert paths in the 'basename' column of a dataframe to relative paths. If the 'basename' column has the following paths: ['/usr/topo/data/a/b', '/usr/topo/data/c/d'], the output will be: ['a/b', 'c/d']. Parameters ---------- df : pd.DataFrame A pandas dataframe containing a column 'basename' which contains the paths indicating the locations of the image data files. Returns ------- pd.DataFrame A pandas dataframe where the 'basename' column has paths relative to a common parent. """ paths = df["basename"].tolist() paths = [Path(path) for path in paths] relative_paths = get_relative_paths(paths=paths) df["basename"] = relative_paths return df
# pylint: disable=too-many-instance-attributes
[docs] class LoadScans: """ Load the image and image parameters from a file path. Parameters ---------- img_paths : list[str, Path] Path to a valid AFM scan to load. channel : str Image channel to extract from the scan. """ def __init__( self, img_paths: list[str | Path], channel: str, ): """ Initialise the class. Parameters ---------- img_paths : list[str | Path] Path to a valid AFM scan to load. channel : str Image channel to extract from the scan. """ self.img_paths = img_paths self.img_path = None self.channel = channel self.channel_data = None self.filename = None self.image = None self.pixel_to_nm_scaling = None self.grain_masks = {} self.grain_trace_data = {} self.img_dict = {} self.MINIMUM_IMAGE_SIZE = 10
[docs] def load_spm(self) -> tuple[npt.NDArray, float]: """ Extract image and pixel to nm scaling from the Bruker .spm file. Returns ------- tuple[npt.NDArray, float] A tuple containing the image and its pixel to nanometre scaling value. """ LOGGER.info(f"Loading image from : {self.img_path}") try: scan = pySPM.Bruker(self.img_path) LOGGER.info(f"[{self.filename}] : Loaded image from : {self.img_path}") self.channel_data = scan.get_channel(self.channel) LOGGER.info(f"[{self.filename}] : Extracted channel {self.channel}") image = np.flipud(np.array(self.channel_data.pixels)) except FileNotFoundError: LOGGER.info(f"[{self.filename}] File not found : {self.img_path}") raise except Exception as e: # trying to return the error with options of possible channel values labels = [] for channel in [layer[b"@2:Image Data"][0] for layer in scan.layers]: channel_description = channel.decode("latin1").split('"')[1] # in case blank field raises questions? labels.append(channel_description) LOGGER.error(f"[{self.filename}] : {self.channel} not in {self.img_path.suffix} channel list: {labels}") raise e return (image, self._spm_pixel_to_nm_scaling(self.channel_data))
[docs] def _spm_pixel_to_nm_scaling(self, channel_data: pySPM.SPM.SPM_image) -> float: """ Extract pixel to nm scaling from the SPM image metadata. Parameters ---------- channel_data : pySPM.SPM.SPM_image Channel data from PySPM. Returns ------- float Pixel to nm scaling factor. """ unit_dict = { "pm": 1e-3, "nm": 1, "um": 1e3, "mm": 1e6, } px_to_real = channel_data.pxs() # Has potential for non-square pixels but not yet implemented pixel_to_nm_scaling = ( px_to_real[0][0] * unit_dict[px_to_real[0][1]], px_to_real[1][0] * unit_dict[px_to_real[1][1]], )[0] if px_to_real[0][0] == 0 and px_to_real[1][0] == 0: pixel_to_nm_scaling = 1 LOGGER.warning(f"[{self.filename}] : Pixel size not found in metadata, defaulting to 1nm") LOGGER.info(f"[{self.filename}] : Pixel to nm scaling : {pixel_to_nm_scaling}") return pixel_to_nm_scaling
[docs] def load_topostats(self) -> tuple[npt.NDArray, float]: """ Load a .topostats file (hdf5 format). Loads and extracts the image, pixel to nanometre scaling factor and any grain masks. Note that grain masks are stored via self.grain_masks rather than returned due to how we extract information for all other file loading functions. Returns ------- tuple[npt.NDArray, float] A tuple containing the image and its pixel to nanometre scaling value. """ LOGGER.info(f"Loading image from : {self.img_path}") try: with h5py.File(self.img_path, "r") as f: # Load the hdf5 data to dictionary topodata = hdf5_to_dict(open_hdf5_file=f, group_path="/") main_keys = topodata.keys() file_version = topodata["topostats_file_version"] LOGGER.info(f"TopoStats file version: {file_version}") image = topodata["image"] pixel_to_nm_scaling = topodata["pixel_to_nm_scaling"] if "grain_masks" in main_keys: grain_masks_keys = topodata["grain_masks"].keys() if "above" in grain_masks_keys: LOGGER.info(f"[{self.filename}] : Found grain mask for above direction") self.grain_masks["above"] = topodata["grain_masks"]["above"] if "below" in grain_masks_keys: LOGGER.info(f"[{self.filename}] : Found grain mask for below direction") self.grain_masks["below"] = topodata["grain_masks"]["below"] if "grain_trace_data" in main_keys: LOGGER.info(f"[{self.filename}] : Found grain trace data") self.grain_trace_data = topodata["grain_trace_data"] except OSError as e: if "Unable to open file" in str(e): LOGGER.info(f"[{self.filename}] File not found: {self.img_path}") raise e return (image, pixel_to_nm_scaling)
[docs] def load_asd(self) -> tuple[npt.NDArray, float]: """ Extract image and pixel to nm scaling from .asd files. Returns ------- tuple[npt.NDArray, float] A tuple containing the image and its pixel to nanometre scaling value. """ try: frames: np.ndarray pixel_to_nm_scaling: float _: dict frames, pixel_to_nm_scaling, _ = asd.load_asd(file_path=self.img_path, channel=self.channel) LOGGER.info(f"[{self.filename}] : Loaded image from : {self.img_path}") except FileNotFoundError: LOGGER.info(f"[{self.filename}] : File not found. Path: {self.img_path}") raise return (frames, pixel_to_nm_scaling)
[docs] def load_ibw(self) -> tuple[npt.NDArray, float]: """ Load image from Asylum Research (Igor) .ibw files. Returns ------- tuple[npt.NDArray, float] A tuple containing the image and its pixel to nanometre scaling value. """ LOGGER.info(f"Loading image from : {self.img_path}") try: scan = binarywave.load(self.img_path) LOGGER.info(f"[{self.filename}] : Loaded image from : {self.img_path}") labels = [] for label_list in scan["wave"]["labels"]: for label in label_list: if label: labels.append(label.decode()) channel_idx = labels.index(self.channel) image = scan["wave"]["wData"][:, :, channel_idx].T * 1e9 # Looks to be in m image = np.flipud(image) LOGGER.info(f"[{self.filename}] : Extracted channel {self.channel}") except FileNotFoundError: LOGGER.info(f"[{self.filename}] File not found : {self.img_path}") except ValueError: LOGGER.error(f"[{self.filename}] : {self.channel} not in {self.img_path.suffix} channel list: {labels}") raise except Exception as exception: LOGGER.error(f"[{self.filename}] : {exception}") return (image, self._ibw_pixel_to_nm_scaling(scan))
[docs] def _ibw_pixel_to_nm_scaling(self, scan: dict) -> float: """ Extract pixel to nm scaling from the IBW image metadata. Parameters ---------- scan : dict The loaded binary wave object. Returns ------- float A value corresponding to the real length of a single pixel. """ # Get metadata notes = {} for line in str(scan["wave"]["note"]).split("\\r"): if line.count(":"): key, val = line.split(":", 1) notes[key] = val.strip() # Has potential for non-square pixels but not yet implemented pixel_to_nm_scaling = ( float(notes["SlowScanSize"]) / scan["wave"]["wData"].shape[0] * 1e9, # as in m float(notes["FastScanSize"]) / scan["wave"]["wData"].shape[1] * 1e9, # as in m )[0] LOGGER.info(f"[{self.filename}] : Pixel to nm scaling : {pixel_to_nm_scaling}") return pixel_to_nm_scaling
[docs] def load_jpk(self) -> tuple[npt.NDArray, float]: """ Load image from JPK Instruments .jpk files. Returns ------- tuple[npt.NDArray, float] A tuple containing the image and its pixel to nanometre scaling value. """ # Load the file img_path = str(self.img_path) try: tif = tifffile.TiffFile(img_path) except FileNotFoundError: LOGGER.info(f"[{self.filename}] File not found : {self.img_path}") raise # Obtain channel list for all channels in file channel_list = {} for i, page in enumerate(tif.pages[1:]): # [0] is thumbnail available_channel = page.tags["32848"].value # keys are hexadecimal values if page.tags["32849"].value == 0: # whether img is trace or retrace tr_rt = "trace" else: tr_rt = "retrace" channel_list[f"{available_channel}_{tr_rt}"] = i + 1 try: channel_idx = channel_list[self.channel] except KeyError: LOGGER.error(f"{self.channel} not in channel list: {channel_list}") raise # Get image and if applicable, scale it channel_page = tif.pages[channel_idx] image = channel_page.asarray() scaling_type = channel_page.tags["33027"].value if scaling_type == "LinearScaling": scaling = channel_page.tags["33028"].value offset = channel_page.tags["33029"].value image = (image * scaling) + offset elif scaling_type == "NullScaling": pass else: raise ValueError(f"Scaling type {scaling_type} is not 'NullScaling' or 'LinearScaling'") # Get page for common metadata between scans metadata_page = tif.pages[0] return (image * 1e9, self._jpk_pixel_to_nm_scaling(metadata_page))
[docs] @staticmethod def _jpk_pixel_to_nm_scaling(tiff_page: tifffile.tifffile.TiffPage) -> float: """ Extract pixel to nm scaling from the JPK image metadata. Parameters ---------- tiff_page : tifffile.tifffile.TiffPage An image file directory (IFD) of .jpk files. Returns ------- float A value corresponding to the real length of a single pixel. """ length = tiff_page.tags["32834"].value # Grid-uLength (fast) width = tiff_page.tags["32835"].value # Grid-vLength (slow) length_px = tiff_page.tags["32838"].value # Grid-iLength (fast) width_px = tiff_page.tags["32839"].value # Grid-jLength (slow) px_to_nm = (length / length_px, width / width_px)[0] LOGGER.info(px_to_nm) return px_to_nm * 1e9
[docs] @staticmethod def _gwy_read_object(open_file: io.TextIOWrapper, data_dict: dict) -> None: """ Parse and extract data from a `.gwy` file object, starting at the current open file read position. Parameters ---------- open_file : io.TextIOWrapper An open file object. data_dict : dict Dictionary of `.gwy` file image properties. """ object_name = read_null_terminated_string(open_file=open_file) data_size = read_u32i(open_file) LOGGER.debug(f"OBJECT | name: {object_name} | data_size: {data_size}") # Read components read_data_size = 0 while read_data_size < data_size: component_data_size = LoadScans._gwy_read_component( open_file=open_file, initial_byte_pos=open_file.tell(), data_dict=data_dict, ) read_data_size += component_data_size
[docs] @staticmethod def _gwy_read_component(open_file: io.TextIOWrapper, initial_byte_pos: int, data_dict: dict) -> int: """ Parse and extract data from a `.gwy` file object, starting at the current open file read position. Parameters ---------- open_file : io.TextIOWrapper An open file object. initial_byte_pos : int Initial position, as byte. data_dict : dict Dictionary of `.gwy` file image properties. Returns ------- int Size of the component in bytes. """ component_name = read_null_terminated_string(open_file=open_file) data_type = read_gwy_component_dtype(open_file=open_file) if data_type == "o": LOGGER.debug(f"component name: {component_name} | dtype: {data_type} |") sub_dict = {} LoadScans._gwy_read_object(open_file=open_file, data_dict=sub_dict) data_dict[component_name] = sub_dict elif data_type == "c": value = read_char(open_file=open_file) LOGGER.debug(f"component name: {component_name} | dtype: {data_type} | value: {value}") data_dict[component_name] = value elif data_type == "i": value = read_u32i(open_file=open_file) LOGGER.debug(f"component name: {component_name} | dtype: {data_type} | value: {value}") data_dict[component_name] = value elif data_type == "d": value = read_64d(open_file=open_file) LOGGER.debug(f"component name: {component_name} | dtype: {data_type} | value: {value}") data_dict[component_name] = value elif data_type == "s": value = read_null_terminated_string(open_file=open_file) LOGGER.debug(f"component name: {component_name} | dtype: {data_type} | value: {value}") data_dict[component_name] = value elif data_type == "D": array_size = read_u32i(open_file=open_file) LOGGER.debug(f"component name: {component_name} | dtype: {data_type}") LOGGER.debug(f"array size: {array_size}") data = np.zeros(array_size) for index in range(array_size): data[index] = read_64d(open_file=open_file) if "xres" in data_dict and "yres" in data_dict: data = data.reshape((data_dict["xres"], data_dict["yres"])) data_dict["data"] = data return open_file.tell() - initial_byte_pos
[docs] @staticmethod def _gwy_print_dict(gwy_file_dict: dict, pre_string: str) -> None: """ Recursively print nested dictionary. Can be used to find labels and values of objects / components in the `.gwy` file. Parameters ---------- gwy_file_dict : dict Dictionary of the nested object / component structure of a `.gwy` file. pre_string : str Prefix to use when printing string. """ for key, value in gwy_file_dict.items(): if isinstance(value, dict): print(pre_string + f"OBJECT: {key}") pre_string += " " LoadScans._gwy_print_dict(gwy_file_dict=value, pre_string=pre_string) pre_string = pre_string[:-2] else: print(pre_string + f"component: {key} | value: {value}")
[docs] @staticmethod def _gwy_print_dict_wrapper(gwy_file_dict: dict) -> None: """ Print dictionaries. This is a wrapper for the _gwy_print_dict() method. Parameters ---------- gwy_file_dict : dict Dictionary of the nested object / component structure of a `.gwy` file. """ pre_string = "" LoadScans._gwy_print_dict(gwy_file_dict=gwy_file_dict, pre_string=pre_string)
[docs] @staticmethod def _gwy_get_channels(gwy_file_structure: dict) -> dict: """ Extract a list of channels and their corresponding dictionary key ids from the `.gwy` file dictionary. Parameters ---------- gwy_file_structure : dict Dictionary of the nested object / component structure of a `.gwy` file. Where the keys are object names and the values are dictionaries of the object's components. Returns ------- dict Dictionary where the keys are the channel names and the values are the dictionary key ids. Examples -------- # Using a loaded dictionary generated from a `.gwy` file: LoadScans._gwy_get_channels(gwy_file_structure=loaded_gwy_file_dictionary) """ title_key_pattern = re.compile(r"\d+(?=/data/title)") channel_ids = {} for key, _ in gwy_file_structure.items(): match = re.search(title_key_pattern, key) if match: channel = gwy_file_structure[key] channel_ids[channel] = match.group() return channel_ids
[docs] def load_gwy(self) -> tuple[npt.NDArray, float]: """ Extract image and pixel to nm scaling from the Gwyddion .gwy file. Returns ------- tuple[npt.NDArray, float] A tuple containing the image and its pixel to nanometre scaling value. """ LOGGER.info(f"Loading image from : {self.img_path}") try: image_data_dict = {} with Path.open(self.img_path, "rb") as open_file: # pylint: disable=unspecified-encoding # Read header header = open_file.read(4) LOGGER.debug(f"Gwy file header: {header}") LoadScans._gwy_read_object(open_file, data_dict=image_data_dict) # For development - uncomment to have an indentation based nested # dictionary output showing the object - component structure and # available keys: # LoadScans._gwy_print_dict_wrapper(gwy_file_dict=image_data_dict) channel_ids = LoadScans._gwy_get_channels(gwy_file_structure=image_data_dict) if self.channel not in channel_ids: raise KeyError( f"Channel {self.channel} not found in {self.img_path.suffix} channel list: {channel_ids}" ) # Get the image data image = image_data_dict[f"/{channel_ids[self.channel]}/data"]["data"] units = image_data_dict[f"/{channel_ids[self.channel]}/data"]["si_unit_xy"]["unitstr"] # currently only support equal pixel sizes in x and y px_to_nm = image_data_dict[f"/{channel_ids[self.channel]}/data"]["xreal"] / image.shape[1] # Convert image heights to nanometresQ if units == "m": image = image * 1e9 px_to_nm = px_to_nm * 1e9 else: raise ValueError( f"Units '{units}' have not been added for .gwy files. Please add \ an SI to nanometre conversion factor for these units in _gwy_read_component in \ io.py." ) except FileNotFoundError: LOGGER.info(f"[{self.filename}] File not found : {self.img_path}") raise return (image, px_to_nm)
[docs] def get_data(self) -> None: """Extract image, filepath and pixel to nm scaling value, and append these to the img_dic object.""" suffix_to_loader = { ".spm": self.load_spm, ".jpk": self.load_jpk, ".ibw": self.load_ibw, ".gwy": self.load_gwy, ".topostats": self.load_topostats, ".asd": self.load_asd, } for img_path in self.img_paths: self.img_path = img_path self.filename = img_path.stem suffix = img_path.suffix LOGGER.info(f"Extracting image from {self.img_path}") LOGGER.debug(f"File extension : {suffix}") # Check that the file extension is supported if suffix in suffix_to_loader: try: self.image, self.pixel_to_nm_scaling = suffix_to_loader[suffix]() except Exception as e: if "Channel" in str(e) and "not found" in str(e): LOGGER.warning(e) # log the specific error message LOGGER.warning(f"[{self.filename}] Channel {self.channel} not found, skipping image.") else: raise else: if suffix == ".asd": for index, frame in enumerate(self.image): self._check_image_size_and_add_to_dict(image=frame, filename=f"{self.filename}_{index}") else: self._check_image_size_and_add_to_dict(image=self.image, filename=self.filename) else: raise ValueError( f"File type {suffix} not yet supported. Please make an issue at \ https://github.com/AFM-SPM/TopoStats/issues, or email topostats@sheffield.ac.uk to request support for \ this file type." )
[docs] def _check_image_size_and_add_to_dict(self, image: npt.NDArray, filename: str) -> None: """ Check the image is above a minimum size in both dimensions. Images that do not meet the minimum size are not included for processing. Parameters ---------- image : npt.NDArray An array of the extracted AFM image. filename : str The name of the file. """ if image.shape[0] < self.MINIMUM_IMAGE_SIZE or image.shape[1] < self.MINIMUM_IMAGE_SIZE: LOGGER.warning(f"[{filename}] Skipping, image too small: {image.shape}") else: self.add_to_dict(image=image, filename=filename) LOGGER.info(f"[{filename}] Image added to processing.")
[docs] def add_to_dict(self, image: npt.NDArray, filename: str) -> None: """ Add an image and metadata to the img_dict dictionary under the key filename. Adds the image and associated metadata such as any grain masks, and pixel to nanometere scaling factor to the img_dict dictionary which is used as a place to store the image information for processing. Parameters ---------- image : npt.NDArray An array of the extracted AFM image. filename : str The name of the file. """ self.img_dict[filename] = { "filename": filename, "img_path": self.img_path.with_name(filename), "pixel_to_nm_scaling": self.pixel_to_nm_scaling, "image_original": image, "image_flattened": None, "grain_masks": self.grain_masks, "grain_trace_data": self.grain_trace_data, }
[docs] def dict_to_hdf5(open_hdf5_file: h5py.File, group_path: str, dictionary: dict) -> None: """ Recursively save a dictionary to an open hdf5 file. Parameters ---------- open_hdf5_file : h5py.File An open hdf5 file object. group_path : str The path to the group in the hdf5 file to start saving data from. dictionary : dict A dictionary of the data to save. """ for key, item in dictionary.items(): LOGGER.debug(f"Saving key: {key}") if item is None: LOGGER.warning(f"Item '{key}' is None. Skipping.") # Make sure the key is a string key = str(key) # Check if the item is a known datatype # Ruff wants us to use the pipe operator here but it isn't supported by python 3.9 if isinstance(item, (list, str, int, float, np.ndarray, Path, dict)): # noqa: UP038 # Lists need to be converted to numpy arrays if isinstance(item, list): item = np.array(item) open_hdf5_file[group_path + key] = item # Strings need to be encoded to bytes elif isinstance(item, str): open_hdf5_file[group_path + key] = item.encode("utf8") # Integers, floats and numpy arrays can be added directly to the hdf5 file # Ruff wants us to use the pipe operator here but it isn't supported by python 3.9 elif isinstance(item, (int, float, np.ndarray)): # noqa: UP038 open_hdf5_file[group_path + key] = item # Path objects need to be encoded to bytes elif isinstance(item, Path): open_hdf5_file[group_path + key] = str(item).encode("utf8") # Dictionaries need to be recursively saved elif isinstance(item, dict): # a sub-dictionary, so we need to recurse dict_to_hdf5(open_hdf5_file, group_path + key + "/", item) else: # attempt to save an item that is not a numpy array or a dictionary try: open_hdf5_file[group_path + key] = item except Exception as e: LOGGER.info(f"Cannot save key '{key}' to HDF5. Item type: {type(item)}. Skipping. {e}")
[docs] def hdf5_to_dict(open_hdf5_file: h5py.File, group_path: str) -> dict: """ Read a dictionary from an open hdf5 file. Parameters ---------- open_hdf5_file : h5py.File An open hdf5 file object. group_path : str The path to the group in the hdf5 file to start reading data from. Returns ------- dict A dictionary of the hdf5 file data. """ data_dict = {} for key, item in open_hdf5_file[group_path].items(): LOGGER.info(f"Loading hdf5 key: {key}") if isinstance(item, h5py.Group): LOGGER.info(f" {key} is a group") data_dict[key] = hdf5_to_dict(open_hdf5_file, group_path + key + "/") # Decode byte strings to utf-8. The data type "O" is a byte string. elif isinstance(item, h5py.Dataset) and item.dtype == "O": LOGGER.debug(f" {key} is a byte string") data_dict[key] = item[()].decode("utf-8") LOGGER.debug(f" {key} type: {type(data_dict[key])}") else: LOGGER.debug(f" {key} is other type of dataset") data_dict[key] = item[()] LOGGER.debug(f" {key} type: {type(data_dict[key])}") return data_dict
[docs] def save_topostats_file(output_dir: Path, filename: str, topostats_object: dict) -> None: """ Save a topostats dictionary object to a .topostats (hdf5 format) file. Parameters ---------- output_dir : Path Directory to save the .topostats file in. filename : str File name of the .topostats file. topostats_object : dict Dictionary of the topostats data to save. Must include a flattened image and pixel to nanometre scaling factor. May also include grain masks. """ LOGGER.info(f"[{filename}] : Saving image to .topostats file") if ".topostats" not in filename: save_file_path = output_dir / f"{filename}.topostats" else: save_file_path = output_dir / filename with h5py.File(save_file_path, "w") as f: # It may be possible for topostats_object["image_flattened"] to be None. # Make sure that this is not the case. if topostats_object["image_flattened"] is not None: topostats_object["topostats_file_version"] = 0.2 # Rename the key to "image" for backwards compatibility topostats_object["image"] = topostats_object.pop("image_flattened") # Recursively save the topostats object dictionary to the .topostats file dict_to_hdf5(open_hdf5_file=f, group_path="/", dictionary=topostats_object) else: raise ValueError( "TopoStats object dictionary does not contain an 'image_flattened'. \ TopoStats objects must be saved with a flattened image." )
[docs] def save_pkl(outfile: Path, to_pkl: dict) -> None: """ Pickle objects for working with later. Parameters ---------- outfile : Path Path and filename to save pickle to. to_pkl : dict Object to be picled. """ with outfile.open(mode="wb", encoding=None) as f: pkl.dump(to_pkl, f)
[docs] def load_pkl(infile: Path) -> Any: """ Load data from a pickle. Parameters ---------- infile : Path Path to a valid pickle. Returns ------- dict: Dictionary of generated images. Examples -------- from pathlib import Path from topostats.io import load_plots pkl_path = "output/distribution_plots.pkl" my_plots = load_pkl(pkl_path) # Show the type of my_plots which is a dictionary of nested dictionaries type(my_plots) # Show the keys are various levels of nesting. my_plots.keys() my_plots["area"].keys() my_plots["area"]["dist"].keys() # Get the figure and axis object for a given metrics distribution plot figure, axis = my_plots["area"]["dist"].values() # Get the figure and axis object for a given metrics violin plot figure, axis = my_plots["area"]["violin"].values() """ with infile.open("rb", encoding=None) as f: return pkl.load(f) # noqa: S301