deploy: phasorpy/phasorpy@2e3fa61

Author: cgohlke

docs/main/_downloads/18de1ed1105dda1f0c5220a278448c20/phasorpy_phasor_from_lifetime.zip

@@ -194,7 +194,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## PicoQuant PTU\n\nPicoQuant PTU files are written by PicoQuant SymPhoTime, Leica LAS X, and\nother software. The files contain time-correlated single-photon\ncounting (TCSPC) measurement data and instrumentation parameters.\n\nPhasorPy supports reading TCSPC histograms from PTU files acquired in T3\nimaging mode via the [ptufile](https://github.com/cgohlke/ptufile/)\nlibrary.\n\nThe :py:func:`phasorpy.io.signal_from_ptu` function is used to read\nthe TCSPC histogram from a PTU file exported from the [FLIM_testdata LIF\ndataset](https://dx.doi.org/10.6084/m9.figshare.22336594.v1).\nFor phasor analysis, all photons in periods with multiple photons must\nbe discarded before exporting to PTU format. In the Leica LAS X software,\nselect the \"FLIM\" tab, click on the \"Phasor\" button, and under\n\"Specialist Settings\" select the option \"Standard (High Speed)\".\nThe first channel in the first frame is read from the PTU file:\n\n"
+        "## PicoQuant PTU\n\nPicoQuant PTU files are written by PicoQuant SymPhoTime, Leica LAS X, and\nother software. The files contain time-correlated single-photon\ncounting (TCSPC) measurement data and instrumentation parameters.\n\nPhasorPy supports reading TCSPC histograms from PTU files acquired in T3\nimaging mode via the [ptufile](https://github.com/cgohlke/ptufile/)\nlibrary.\n\nThe :py:func:`phasorpy.io.signal_from_ptu` function is used to read\nthe TCSPC histogram from a PTU file exported from the [FLIM_testdata LIF\ndataset](https://dx.doi.org/10.6084/m9.figshare.22336594.v1).\nFor phasor analysis, periods containing multiple photons must have all\ntheir photons discarded before exporting to PTU format. In the Leica\nLAS X software, select the \"FLIM\" tab, click on the \"Phasor\" button,\nand under \"Specialist Settings\" select the option \"Standard (High Speed)\".\nThe first channel in the first frame is read from the PTU file:\n\n"
       ]
     },
     {
@@ -309,7 +309,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Becker & Hickl SDT\n\nSDT files are written by Becker & Hickl software.\nThey may contain TCSPC histograms and metadata from laser-scanning\nmicroscopy.\n\nPhasorPy supports reading TCSPC histograms from FBD files via the\n[lfdfiles](https://github.com/cgohlke/lfdfiles/) library.\n\nThe :py:func:`phasorpy.io.signal_from_sdt` function is used to read a\nTCSPC histogram from a SDT file:\n\n"
+        "## Becker & Hickl SDT\n\nSDT files are written by Becker & Hickl software.\nThey may contain TCSPC histograms and metadata from laser-scanning\nmicroscopy.\n\nPhasorPy supports reading TCSPC histograms from SDT files via the\n[sdtfile](https://github.com/cgohlke/sdtfile/) library.\n\nThe :py:func:`phasorpy.io.signal_from_sdt` function is used to read a\nTCSPC histogram from a SDT file:\n\n"
       ]
     },
     {
@@ -352,7 +352,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## FLIMbox FBD\n\nFLIMbox data files, FBD, are written by SimFCS and ISS software.\nThey contain encoded cross-correlation phase histograms from digital\nfrequency-domain measurements acquired with a FLIMbox device.\nNewer file versions also contain metadata.\n\nThe FBD file format is undocumented, not standardized, and files are\nfrequently found corrupted. It is recommended to export FLIMbox data to\nanother format from the software used to acquire the data.\n\nPhasorPy supports reading some FLIMbox FBD files via the\n[lfdfiles](https://github.com/cgohlke/lfdfiles/) library.\n\nThe :py:func:`phasorpy.io.signal_from_fbd` function is used to read\na phase histograms from the\n[Convallaria FBD dataset](https://zenodo.org/records/14026720), which was\nacquired at the second harmonic. The dataset is a time series of two\nchannels. Since the photon count is low and the second channel empty,\nonly the first channel is read and the time-axis integrated:\n\n"
+        "## FLIMbox FBD\n\nFLIMbox data files, FBD, are written by SimFCS and ISS software.\nThey contain encoded cross-correlation phase histograms from digital\nfrequency-domain measurements acquired with a FLIMbox device.\nNewer file versions also contain metadata.\n\nThe FBD file format is undocumented, not standardized, and files are\nfrequently found corrupted. Therefore, it is recommended to export FLIMbox\nfiles to another format from the software used to acquire the data.\n\nPhasorPy supports reading some FLIMbox FBD files via the\n[lfdfiles](https://github.com/cgohlke/lfdfiles/) library.\n\nThe :py:func:`phasorpy.io.signal_from_fbd` function is used to read\na phase histograms from the\n[Convallaria FBD dataset](https://zenodo.org/records/14026720), which was\nacquired at the second harmonic. The dataset is a time series of two\nchannels. Since the photon count is low and the second channel empty,\nonly the first channel is read and the time-axis integrated:\n\n"
       ]
     },
     {
@@ -568,7 +568,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Three main lifetime components are expected in the sample:\nfree NADH (~0.4 ns), bound NADH (3.4 ns) and a long lifetime species (~8 ns).\nIt appears the calibration is off in this sample.\n\n"
+        "Three main lifetime components are expected in the sample: free\nNADH (~0.4 ns), bound NADH (3.4 ns), and a long lifetime species (~8 ns).\nIt appears the calibration is off in this sample.\n\n"
       ]
     },
     {
@@ -629,7 +629,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## PhasorPy OME-TIFF\n\nPhasorPy can store phasor coordinates and select metadata in\n[OME-TIFF](https://ome-model.readthedocs.io/en/stable/ome-tiff/)\nformatted files, which are compatible with Bio-Formats, Fiji, and other\nsoftware. The implementation is based on the\n[tifffile](https://github.com/cgohlke/tifffile/) library.\n\nIn comparison with the SimFCS R64 format, OME-TIFF can store higher\ndimensional, higher precision images of any size, any number of harmonics,\nand select metadata.\n\nPhasorPy OME-TIFF files are intended for temporarily exchanging phasor\ncoordinates with other software, not as a long-term storage solution.\nAlways preserve original data files in their native formats.\n\nThe :py:func:`phasorpy.io.phasor_to_ometiff` and\n:py:func:`phasorpy.io.phasor_from_ometiff` functions are used to write and\nread back calibrated phasor coordinates to/from PhasorPy OME-TIFF files:\n\n"
+        "## PhasorPy OME-TIFF\n\nPhasorPy can store phasor coordinates and select metadata in\n[OME-TIFF](https://ome-model.readthedocs.io/en/stable/ome-tiff/)\nformatted files, which are compatible with Bio-Formats, Fiji, and other\nsoftware. The implementation is based on the\n[tifffile](https://github.com/cgohlke/tifffile/) library.\n\nCompared to the SimFCS R64 format, OME-TIFF offers several advantages.\nIt can store higher-dimensional, higher-precision images of any size,\nany number of harmonics, and selected metadata.\n\nPhasorPy OME-TIFF files are intended for temporarily exchanging phasor\ncoordinates with other software, not as a long-term storage solution.\nAlways preserve original data files in their native formats.\n\nThe :py:func:`phasorpy.io.phasor_to_ometiff` and\n:py:func:`phasorpy.io.phasor_from_ometiff` functions are used to write and\nread back calibrated phasor coordinates to/from PhasorPy OME-TIFF files:\n\n"
       ]
     },
     {
@@ -640,7 +640,7 @@
       },
       "outputs": [],
       "source": [
-        "from phasorpy.io import phasor_from_ometiff, phasor_to_ometiff\n\nfilename = f'{filename}.ome.tiff'\n\nwith TemporaryDirectory() as tmpdir:\n\n    phasor_to_ometiff(\n        filename,\n        mean,\n        real,\n        imag,\n        frequency=frequency,\n        dims='YX',\n        description='Written by PhasorPy',\n    )\n\n    mean1, real1, imag1, attrs = phasor_from_ometiff(filename, harmonic='all')\n    assert_allclose(mean, mean1)\n    assert attrs['frequency'] == frequency\n    assert attrs['harmonic'] == [1, 2]\n    assert attrs['description'] == 'Written by PhasorPy'"
+        "from phasorpy.io import phasor_from_ometiff, phasor_to_ometiff\n\nwith TemporaryDirectory() as tmpdir:\n\n    filename = os.path.join(tmpdir, 'capillaries1001.ome.tif')\n\n    phasor_to_ometiff(\n        filename,\n        mean,\n        real,\n        imag,\n        frequency=frequency,\n        dims='YX',\n        description='Written by PhasorPy',\n    )\n\n    mean1, real1, imag1, attrs = phasor_from_ometiff(filename, harmonic='all')\n    assert_allclose(mean, mean1)\n    assert attrs['frequency'] == frequency\n    assert attrs['harmonic'] == [1, 2]\n    assert attrs['description'] == 'Written by PhasorPy'"
       ]
     },
     {

docs/main/_downloads/1b3c132ff7b40bb13bb60712fdfbc4bc/phasorpy_io.ipynb

@@ -206,10 +206,10 @@
 # The :py:func:`phasorpy.io.signal_from_ptu` function is used to read
 # the TCSPC histogram from a PTU file exported from the `FLIM_testdata LIF
 # dataset <https://dx.doi.org/10.6084/m9.figshare.22336594.v1>`_.
-# For phasor analysis, all photons in periods with multiple photons must
-# be discarded before exporting to PTU format. In the Leica LAS X software,
-# select the "FLIM" tab, click on the "Phasor" button, and under
-# "Specialist Settings" select the option "Standard (High Speed)".
+# For phasor analysis, periods containing multiple photons must have all
+# their photons discarded before exporting to PTU format. In the Leica
+# LAS X software, select the "FLIM" tab, click on the "Phasor" button,
+# and under "Specialist Settings" select the option "Standard (High Speed)".
 # The first channel in the first frame is read from the PTU file:
 
 from phasorpy.io import signal_from_ptu
@@ -327,8 +327,8 @@
 # They may contain TCSPC histograms and metadata from laser-scanning
 # microscopy.
 #
-# PhasorPy supports reading TCSPC histograms from FBD files via the
-# `lfdfiles <https://github.com/cgohlke/lfdfiles/>`_ library.
+# PhasorPy supports reading TCSPC histograms from SDT files via the
+# `sdtfile <https://github.com/cgohlke/sdtfile/>`_ library.
 #
 # The :py:func:`phasorpy.io.signal_from_sdt` function is used to read a
 # TCSPC histogram from a SDT file:
@@ -368,8 +368,8 @@
 # Newer file versions also contain metadata.
 #
 # The FBD file format is undocumented, not standardized, and files are
-# frequently found corrupted. It is recommended to export FLIMbox data to
-# another format from the software used to acquire the data.
+# frequently found corrupted. Therefore, it is recommended to export FLIMbox
+# files to another format from the software used to acquire the data.
 #
 # PhasorPy supports reading some FLIMbox FBD files via the
 # `lfdfiles <https://github.com/cgohlke/lfdfiles/>`_ library.
@@ -592,8 +592,8 @@
 )
 
 # %%
-# Three main lifetime components are expected in the sample:
-# free NADH (~0.4 ns), bound NADH (3.4 ns) and a long lifetime species (~8 ns).
+# Three main lifetime components are expected in the sample: free
+# NADH (~0.4 ns), bound NADH (3.4 ns), and a long lifetime species (~8 ns).
 # It appears the calibration is off in this sample.
 
 # %%
@@ -679,9 +679,9 @@
 # software. The implementation is based on the
 # `tifffile <https://github.com/cgohlke/tifffile/>`_ library.
 #
-# In comparison with the SimFCS R64 format, OME-TIFF can store higher
-# dimensional, higher precision images of any size, any number of harmonics,
-# and select metadata.
+# Compared to the SimFCS R64 format, OME-TIFF offers several advantages.
+# It can store higher-dimensional, higher-precision images of any size,
+# any number of harmonics, and selected metadata.
 #
 # PhasorPy OME-TIFF files are intended for temporarily exchanging phasor
 # coordinates with other software, not as a long-term storage solution.
@@ -693,10 +693,10 @@
 
 from phasorpy.io import phasor_from_ometiff, phasor_to_ometiff
 
-filename = f'{filename}.ome.tiff'
-
 with TemporaryDirectory() as tmpdir:
 
+    filename = os.path.join(tmpdir, 'capillaries1001.ome.tif')
+
     phasor_to_ometiff(
         filename,
         mean,