Source code for drymass.converter

import numbers
from os import fspath
import pathlib

import numpy as np
import qpformat
import qpimage
from skimage.external import tifffile

#: Output qpimage.QPSeries sensor data
FILE_SENSOR_DATA_H5 = "sensor_data.h5"
#: Output phase/amplitude TIFF sensor data
FILE_SENSOR_DATA_TIF = "sensor_data.tif"

[docs]def convert(path_in, dir_out, meta_data={}, holo_kw={}, bg_data_amp=None, bg_data_pha=None, write_tif=False, ret_dataset=False, ret_changed=False): """Convert experimental data to `qpimage.QPSeries` on disk Parameters ---------- bg_data_amp, bg_data_pha: None, int, or path to file The background data for phase and amplitude. One of - `None`: No background data - `int`: Image index (starting at 0) of the input data set to use as background data - `str`, `pathlib.Path`: Path to a separate file that is used for background correction, relative to the directory in which `path_in` is located (`path_in.parent`). """ path = pathlib.Path(path_in).resolve() dout = pathlib.Path(dir_out).resolve() h5out = dout / FILE_SENSOR_DATA_H5 imout = dout / FILE_SENSOR_DATA_TIF ds = qpformat.load_data(path=path, meta_data=meta_data, holo_kw=holo_kw) if not (bg_data_amp is None and bg_data_pha is None): # Only set background of data set if there is # a background defined. bgamp = get_background(bg_data=bg_data_amp, dataset=ds, which="amplitude") bgpha = get_background(bg_data=bg_data_pha, dataset=ds, which="phase") bg_data = qpimage.QPImage(data=(bgpha, bgamp), which_data=("phase", "amplitude")) ds.set_bg(bg_data) if h5out.exists(): with qpimage.QPSeries(h5file=h5out, h5mode="r") as qpsr: if (ds.identifier == qpsr.identifier and len(ds) == len(qpsr)): # file has same identifier and same number of qpimages create = False else: create = True else: create = True if create: # Write h5 data ds.saveh5(h5out) if write_tif and (create or not imout.exists()): # Also write tif data h5series2tif(h5in=h5out, tifout=imout) ret = [h5out] if ret_dataset: ret.append(ds) if ret_changed: ret.append(create) if len(ret) == 1: ret = ret[0] return ret
[docs]def get_background(bg_data, dataset, which="phase"): """Obtain the background data for a dataset Parameters ---------- bg_data: None, int, str, or pathlib.Path Represents the background data: - None: no background data - int: image with this index in `dataset` is used for background correction - str, pathlib.Path: An external file will be used for background correction. dataset: qpformat.dataset.SeriesData The dataset for which the background data is collected. No background correction is performed! `dataset` is needed for integer `bg_data` and for path-based `bg_data` (because of meta data and hologram kwargs). Returns ------- bg: 2d np.ndarray The background data. """ if which not in ["phase", "amplitude"]: raise ValueError("`which` must be 'phase' or 'amplitude'!") if bg_data is None: bg = np.ones(dataset.get_qpimage(0).shape) elif isinstance(bg_data, numbers.Integral): if bg_data < 0 or bg_data > (len(dataset)-1): msg = "Background {} index must be between 0 and {}!" raise ValueError(msg.format(which, len(dataset)-1)) # indexing in configuration file starts at 0 if which == "phase": bg = dataset.get_qpimage(bg_data).pha else: bg = dataset.get_qpimage(bg_data).amp elif isinstance(bg_data, (str, pathlib.Path)): bgpath = pathlib.Path(bg_data) dsbg = qpformat.load_data(path=bgpath, meta_data=dataset.meta_data, holo_kw=dataset.holo_kw) if len(dsbg) != 1: msg = "Background correction with series data not implemented!" raise NotImplementedError(msg) else: if which == "phase": bg = dsbg.get_qpimage(0).pha else: bg = dsbg.get_qpimage(0).amp else: msg = "Unknown type for {} `bg_data`: {}".format(which, bg_data) raise ValueError(msg) return bg
[docs]def h5series2tif(h5in, tifout): """Convert a qpimage.QPSeries file to a phase/amplitude TIFF file""" with qpimage.QPSeries(h5file=h5in, h5mode="r") as qps, \ tifffile.TiffWriter(fspath(tifout), imagej=True) as tf: for ii in range(len(qps)): qpi = qps[ii] res = 1 / qpi["pixel size"] * 1e-6 # use µm dshape = (1, qpi.shape[0], qpi.shape[1]) dataa = np.array(qpi.amp, dtype=np.float32).reshape(*dshape) datap = np.array(qpi.pha, dtype=np.float32).reshape(*dshape) data = np.vstack((datap, dataa)), resolution=(res, res, None))