"""Functions for reading and writing data."""
from __future__ import annotations
import io
import json
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 numpyencoder import NumpyEncoder
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
# Sylvia: Ruff says too complex but I think breaking this out would be more complex.
[docs]
def dict_almost_equal(dict1: dict, dict2: dict, abs_tol: float = 1e-9): # noqa: C901
"""
Recursively check if two dictionaries are almost equal with a given absolute tolerance.
Parameters
----------
dict1 : dict
First dictionary to compare.
dict2 : dict
Second dictionary to compare.
abs_tol : float
Absolute tolerance to check for equality.
Returns
-------
bool
True if the dictionaries are almost equal, False otherwise.
"""
if dict1.keys() != dict2.keys():
return False
LOGGER.info("Comparing dictionaries")
for key in dict1:
LOGGER.info(f"Comparing key {key}")
if isinstance(dict1[key], dict) and isinstance(dict2[key], dict):
if not dict_almost_equal(dict1[key], dict2[key], abs_tol=abs_tol):
return False
elif isinstance(dict1[key], np.ndarray) and isinstance(dict2[key], np.ndarray):
if not np.allclose(dict1[key], dict2[key], atol=abs_tol):
LOGGER.info(f"Key {key} type: {type(dict1[key])} not equal: {dict1[key]} != {dict2[key]}")
return False
elif isinstance(dict1[key], float) and isinstance(dict2[key], float):
# Skip if both values are NaN
if not (np.isnan(dict1[key]) and np.isnan(dict2[key])):
# Check if both values are close
if not np.isclose(dict1[key], dict2[key], atol=abs_tol):
LOGGER.info(f"Key {key} type: {type(dict1[key])} not equal: {dict1[key]} != {dict2[key]}")
return False
elif dict1[key] != dict2[key]:
LOGGER.info(f"Key {key} not equal: {dict1[key]} != {dict2[key]}")
return False
return True
[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 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))
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))
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))
@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
@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
@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
@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}")
@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)
@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."
)
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.info(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.warning(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
[docs]
def dict_to_json(data: dict, output_dir: str | Path, filename: str | Path, indent: int = 4) -> None:
"""
Write a dictionary to a JSON file at the specified location with the given name.
NB : The `NumpyEncoder` class is used as the default encoder to ensure Numpy dtypes are written as strings (they are
not serialisable to JSON using the default JSONEncoder).
Parameters
----------
data : dict
Data as a dictionary that is to be written to file.
output_dir : str | Path
Directory the file is to be written to.
filename : str | Path
Name of output file.
indent : int
Spaces to indent JSON with, default is 4.
"""
output_file = output_dir / filename
with output_file.open("w") as f:
json.dump(data, f, indent=indent, cls=NumpyEncoder)