import numbers
from os import fspath
import pathlib
import warnings
import appdirs
import numpy as np
import pyfftw
import qpformat
import qpimage
import tifffile
from . import util
#: 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"
CACHE_DIR = pathlib.Path(appdirs.user_cache_dir(appname="drymass"))
CACHE_DIR.mkdir(parents=True, exist_ok=True)
PYFFTW_WIDOM_PATH = CACHE_DIR / "pyfftw.wisdom"
if PYFFTW_WIDOM_PATH.exists():
pyfftw.import_wisdom(
[w.encode() for w in PYFFTW_WIDOM_PATH.read_text().split("\t")])
[docs]def convert(path_in, dir_out, meta_data=None, holo_kw=None, qpretrieve_kw=None,
bg_data_amp=None, bg_data_pha=None, write_tif=False,
ret_dataset=False, ret_changed=False, count=None, max_count=None):
"""Convert experimental data to `qpimage.QPSeries` on disk
Parameters
----------
path_in: str or pathlib.Path
Input path to file or directory
dir_out: str or pathlib.Path
Outuput direcory
meta_data: dict
Metadata (see `qpimage.meta.META_KEYS`)
qpretrieve_kw: dict
Keyword arguments passed to
:ref:`qpretrieve <qpretrieve:index>` for
phase retrieval from interferometric data.
holo_kw: dict
This is deprecated, please use `qpretrieve_kw` instead.
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 1) 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`).
write_tif: bool
Export tif images for use with Fiji/ImageJ (tif images
are only created if they don't already exist or if the
analysis changed)
ret_dataset: bool
Return the qpformat dataset
ret_changed: bool
Return True if the dataset changed
count, max_count: multiprocessing.Value
Can be used to monitor the progress of the algorithm.
Initially, the value of `max_count.value` is incremented
by the total number of steps. At each step, the value
of `count.value` is incremented.
"""
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,
qpretrieve_kw=qpretrieve_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 util.is_series_file(h5out):
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 QPSeries
create = False
else:
create = True
else:
create = True
tif_count = max(1, len(ds)//10)
if max_count is not None:
with max_count.get_lock():
max_count.value += len(ds)
max_count.value += tif_count
if create:
# Write h5 data
ds.saveh5(h5out, count=count)
else:
if count is not None:
with count.get_lock():
count.value += len(ds)
if write_tif and (create or not imout.exists()):
# Also write tif data
h5series2tif(h5in=h5out, tifout=imout)
if count is not None:
with count.get_lock():
count.value += tif_count
ret = [h5out]
if ret_dataset:
ret.append(ds)
if ret_changed:
ret.append(create)
if len(ret) == 1:
ret = ret[0]
PYFFTW_WIDOM_PATH.write_text(
"\t".join([w.decode() for w in pyfftw.export_wisdom()]))
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 index `bg_data - 1` 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 < 1 or bg_data > len(dataset):
msg = "Background {} index must be between 1 and {}!"
raise ValueError(msg.format(which, len(dataset)))
# indexing in configuration file starts at 1
if which == "phase":
bg = dataset.get_qpimage(bg_data - 1).pha
else:
bg = dataset.get_qpimage(bg_data - 1).amp
elif isinstance(bg_data, (str, pathlib.Path)):
bgpath = pathlib.Path(bg_data)
dsbg = qpformat.load_data(path=bgpath,
meta_data=dataset.meta_data,
qpretrieve_kw=dataset.qpretrieve_kw)
if len(dsbg) != 1:
warnings.warn(
"Background correction with series data not implemented, "
+ "using first image")
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))
tf.save(data=data,
resolution=(res, res, None),
compress=0,
)