deploy: phasorpy/phasorpy@2d426ec

Author: cgohlke

docs/main/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 8dcaeebed6871cfbb1fb30cc8ec519e7
+config: 11b386b2106af7ff3baa6d66ce2982eb
 tags: d77d1c0d9ca2f4c8421862c7c5a0d620

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

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Multidimensional phasor approach\n\nSimultaneous phasor analysis of lifetime and spectral data.\n\nMultidimensional phasor analysis enables the correlation and classification\nof pixels based on multiple fluorescence characteristics simultaneously.\nThis approach combines phasor coordinates from different measurement domains\nto provide enhanced discrimination and identification of molecular species\nor cellular components that may appear similar in single-domain analysis.\n\nPhasor coordinates in one dimension can be mapped to other dimensions by masks\n(or cursors) that define regions of interest in the phasor space. The dimension\nwhere cursors are defined serves as the \"main\" dimension, controlling the\nclassification that is then applied to correlate with other dimensions.\n\nThis analysis method is demonstrated using a dataset combining fluorescence\nlifetime imaging microscopy (FLIM) and hyperspectral imaging (HSI) of LAURDAN\nfluorescence as presented in:\n\n  Malacrida L, Jameson D, and Gratton E.\n  A multidimensional phasor approach reveals LAURDAN photophysics in NIH-3T3\n  cell membranes.\n  *Sci Rep*, 7: 9215 (2017). https://doi.org/10.1038/s41598-017-08564-z\n\nThe dataset is available at https://zenodo.org/records/16894639.\n\nThe multidimensional analysis correlates FLIM phasor coordinates from two\ndetection channels with spectral phasor coordinates, enabling enhanced\nclassification of cellular regions based on both lifetime and spectral\nproperties of LAURDAN fluorescence.\n"
+        "\n# Multidimensional phasor approach\n\nSimultaneous phasor analysis of lifetime and spectral data.\n\nMultidimensional phasor analysis enables the correlation and classification\nof pixels based on multiple fluorescence characteristics simultaneously.\nThis approach combines phasor coordinates from different measurement domains\nto provide enhanced discrimination and identification of molecular species\nor cellular components that may appear similar in single-domain analysis.\n\nPhasor coordinates in one dimension can be mapped to other dimensions by masks\n(or cursors) that define regions of interest in the phasor space. The dimension\nwhere cursors are defined serves as the \"main\" dimension, controlling the\nclassification that is then applied to correlate with other dimensions.\n\nThis analysis method is demonstrated using a dataset combining fluorescence\nlifetime imaging microscopy (FLIM) and hyperspectral imaging (HSI) of LAURDAN\nfluorescence as presented in:\n\n  Malacrida L, Jameson D, and Gratton E.\n  [A multidimensional phasor approach reveals LAURDAN photophysics in NIH-3T3\n  cell membranes](https://doi.org/10.1038/s41598-017-08564-z).\n  *Sci Rep*, 7: 9215 (2017).\n\nThe dataset is available at https://zenodo.org/records/16894639.\n\nThe multidimensional analysis correlates FLIM phasor coordinates from two\ndetection channels with spectral phasor coordinates, enabling enhanced\nclassification of cellular regions based on both lifetime and spectral\nproperties of LAURDAN fluorescence.\n"
       ]
     },
     {
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "spectral_signal = signal_from_lsm(fetch('04 NIH3T3LAURDAN8meanspectra.lsm'))\n\nflim_signal = signal_from_fbd(\n    fetch('04NIH3T3_LAURDAN_000$CC0Z.fbd'),\n    frame=-1,  # integrate all frames\n    channel=None,  # load all channels\n    laser_factor=0.99168,  # override incorrect metadata in FBD file\n)\n\nreference_signal = numpy.stack(\n    [\n        signal_from_fbd(\n            fetch(f'cumarinech{ch}_780LAURDAN_000$CC0Z.fbd'),\n            frame=-1,\n            channel=ch - 1,\n            laser_factor=0.99168,\n        )\n        for ch in (1, 2)\n    ],\n)\n\nfrequency = flim_signal.attrs['frequency'] * flim_signal.attrs['harmonic']"
+        "spectral_signal = signal_from_lsm(fetch('04 NIH3T3LAURDAN8meanspectra.lsm'))\n\nflim_signal = signal_from_fbd(\n    fetch('04NIH3T3_LAURDAN_000$CC0Z.fbd'),\n    frame=-1,  # integrate all frames\n    channel=None,  # load all channels\n    laser_factor=0.99168,  # override incorrect metadata in FBD file\n)\n\nreference_signal = numpy.stack(\n    [\n        signal_from_fbd(\n            fetch(f'cumarinech{ch + 1}_780LAURDAN_000$CC0Z.fbd'),\n            frame=-1,\n            channel=ch,\n            laser_factor=0.99168,\n        )\n        for ch in (0, 1)\n    ],\n)\n\nfrequency = flim_signal.attrs['frequency'] * flim_signal.attrs['harmonic']"
       ]
     },
     {
@@ -94,7 +94,7 @@
       },
       "outputs": [],
       "source": [
-        "plot_phasor(\n    spectral_real,\n    spectral_imag,\n    xlim=(-0.5, 1.05),\n    ylim=(-0.1, 1.05),\n    allquadrants=True,\n    title='Spectral phasor coordinates',\n)"
+        "plot_phasor(\n    spectral_real,\n    spectral_imag,\n    xlim=(-0.5, 1.05),\n    ylim=(-0.1, 1.05),\n    allquadrants=True,\n    title='Spectral phasor',\n)"
       ]
     },
     {
@@ -112,7 +112,7 @@
       },
       "outputs": [],
       "source": [
-        "plot = PhasorPlot(frequency=frequency, title='FLIM phasor coordinates')\nplot.hist2d(flim_real[0], flim_imag[0], cmap='Blues')\nplot.hist2d(flim_real[1], flim_imag[1], cmap='Oranges', alpha=0.5)\nplot.show()"
+        "fig, axs = pyplot.subplots(2, 1, figsize=(6.4, 9))\nfig.suptitle('FLIM phasor')\nfor ch in (0, 1):\n    plot = PhasorPlot(\n        ax=axs[ch], frequency=frequency, title=f'Channel {ch + 1}'\n    )\n    plot.hist2d(flim_real[ch], flim_imag[ch])\nfig.tight_layout()\nplot.show()"
       ]
     },
     {
@@ -130,7 +130,7 @@
       },
       "outputs": [],
       "source": [
-        "spectral_cursor_real = [0.04, 0.24, 0.14]\nspectral_cursor_imag = [0.74, 0.70, 0.72]\nspectral_cursor_radius = 0.05\n\nspectral_masks = mask_from_circular_cursor(\n    spectral_real,\n    spectral_imag,\n    spectral_cursor_real,\n    spectral_cursor_imag,\n    radius=spectral_cursor_radius,\n)"
+        "spectral_cursor_real = [0.04, 0.24, 0.14]\nspectral_cursor_imag = [0.74, 0.70, 0.72]\nspectral_cursor_radius = 0.05\n\nspectral_mask = mask_from_circular_cursor(\n    spectral_real,\n    spectral_imag,\n    spectral_cursor_real,\n    spectral_cursor_imag,\n    radius=spectral_cursor_radius,\n)"
       ]
     },
     {
@@ -148,7 +148,7 @@
       },
       "outputs": [],
       "source": [
-        "plot = PhasorPlot(\n    xlim=(-0.5, 1.05),\n    ylim=(-0.1, 1.05),\n    xticks=None,\n    yticks=None,\n    allquadrants=True,\n    title='Spectral phasor and cursors',\n)\nplot.hist2d(\n    spectral_real,\n    spectral_imag,\n    cmap='Grays',\n    bins=200,\n)\nplot.cursor(\n    spectral_cursor_real,\n    spectral_cursor_imag,\n    radius=spectral_cursor_radius,\n    color=CATEGORICAL[:3],\n    linestyle='-',\n    linewidth=2,\n)\nplot.show()"
+        "plot = PhasorPlot(\n    xlim=(-0.5, 1.05),\n    ylim=(-0.1, 1.05),\n    xticks=None,\n    yticks=None,\n    allquadrants=True,\n    title='Spectral phasor and cursors',\n)\nplot.hist2d(\n    spectral_real,\n    spectral_imag,\n    cmap='Grays',\n    bins=200,\n)\nplot.cursor(\n    spectral_cursor_real,\n    spectral_cursor_imag,\n    radius=spectral_cursor_radius,\n    color=CATEGORICAL[:3],\n    linewidth=2,\n)\nplot.show()"
       ]
     },
     {
@@ -166,7 +166,7 @@
       },
       "outputs": [],
       "source": [
-        "spectral_pseudo_color = pseudo_color(*spectral_masks)\nplot_image(spectral_pseudo_color, title='Spectral pseudo-color image')"
+        "spectral_pseudo_color = pseudo_color(*spectral_mask)\nplot_image(spectral_pseudo_color, title='Spectral pseudo-color image')"
       ]
     },
     {
@@ -184,7 +184,7 @@
       },
       "outputs": [],
       "source": [
-        "fig, axs = pyplot.subplots(2, 1, figsize=(6.4, 8))\nfig.suptitle('Spectral classified FLIM phasor')\n\nfor ch in range(2):\n    plot = PhasorPlot(\n        ax=axs[ch], frequency=frequency, title=f'Channel {ch + 1}'\n    )\n    for i in range(spectral_masks.shape[0]):\n        plot.plot(\n            flim_real[ch][spectral_masks[i]],\n            flim_imag[ch][spectral_masks[i]],\n            color=CATEGORICAL[i],\n            alpha=0.05,\n            markersize=0.5,\n        )\nfig.tight_layout()\nplot.show()"
+        "fig, axs = pyplot.subplots(2, 1, figsize=(6.4, 9))\nfig.suptitle('Spectral classified FLIM phasor')\n\nfor ch in (0, 1):\n    plot = PhasorPlot(\n        ax=axs[ch], frequency=frequency, title=f'Channel {ch + 1}'\n    )\n    for i in range(spectral_mask.shape[0]):\n        plot.plot(\n            flim_real[ch][spectral_mask[i]],\n            flim_imag[ch][spectral_mask[i]],\n            color=CATEGORICAL[i],\n            alpha=0.05,\n            markersize=0.5,\n        )\nfig.tight_layout()\nplot.show()"
       ]
     },
     {
@@ -202,7 +202,7 @@
       },
       "outputs": [],
       "source": [
-        "flim_cursor_real = [0.35, 0.19, 0.27]\nflim_cursor_imag = [0.44, 0.38, 0.41]\nflim_cursor_radius = 0.05\n\nflim_ch1_masks = mask_from_circular_cursor(\n    flim_real[0],\n    flim_imag[0],\n    flim_cursor_real,\n    flim_cursor_imag,\n    radius=flim_cursor_radius,\n)"
+        "flim_cursor_real = [0.35, 0.19, 0.27]\nflim_cursor_imag = [0.44, 0.38, 0.41]\nflim_cursor_radius = 0.05\n\nflim_mask = mask_from_circular_cursor(\n    flim_real[0],\n    flim_imag[0],\n    flim_cursor_real,\n    flim_cursor_imag,\n    radius=flim_cursor_radius,\n)"
       ]
     },
     {
@@ -220,7 +220,7 @@
       },
       "outputs": [],
       "source": [
-        "plot = PhasorPlot(\n    frequency=frequency,\n    title='FLIM phasor and cursors (first channel)',\n)\nplot.hist2d(flim_real[0], flim_imag[0], cmap='Grays', bins=200)\nplot.cursor(\n    flim_cursor_real,\n    flim_cursor_imag,\n    radius=flim_cursor_radius,\n    color=CATEGORICAL[:3],\n    linestyle='-',\n    linewidth=2,\n)\nplot.show()"
+        "plot = PhasorPlot(\n    frequency=frequency,\n    title='FLIM phasor and cursors (first channel)',\n)\nplot.hist2d(flim_real[0], flim_imag[0], cmap='Grays', bins=200)\nplot.cursor(\n    flim_cursor_real,\n    flim_cursor_imag,\n    radius=flim_cursor_radius,\n    color=CATEGORICAL[:3],\n    linewidth=2,\n)\nplot.show()"
       ]
     },
     {
@@ -238,7 +238,7 @@
       },
       "outputs": [],
       "source": [
-        "plot_image(\n    pseudo_color(*flim_ch1_masks),\n    title='FLIM pseudo-color image (first channel)',\n)"
+        "plot_image(\n    pseudo_color(*flim_mask), title='FLIM pseudo-color image (first channel)'\n)"
       ]
     },
     {
@@ -256,7 +256,7 @@
       },
       "outputs": [],
       "source": [
-        "plot = PhasorPlot(\n    xlim=(-0.5, 1.05),\n    ylim=(-0.1, 1.05),\n    allquadrants=True,\n    title='FLIM classified spectral phasor',\n)\n\nfor i in range(flim_ch1_masks.shape[0]):\n    plot.plot(\n        spectral_real[flim_ch1_masks[i]],\n        spectral_imag[flim_ch1_masks[i]],\n        color=CATEGORICAL[i],\n        alpha=0.05,\n        markersize=1,\n    )\n\nplot.show()"
+        "plot = PhasorPlot(\n    xlim=(-0.5, 1.05),\n    ylim=(-0.1, 1.05),\n    allquadrants=True,\n    title='FLIM classified spectral phasor',\n)\n\nfor i in range(flim_mask.shape[0]):\n    plot.plot(\n        spectral_real[flim_mask[i]],\n        spectral_imag[flim_mask[i]],\n        color=CATEGORICAL[i],\n        alpha=0.05,\n        markersize=1,\n    )\n\nplot.show()"
       ]
     },
     {

docs/main/_downloads/19abb37e5abbed9bd2dd4b38aeb4ea74/phasorpy_multidimensional.ipynb

@@ -20,9 +20,9 @@
 fluorescence as presented in:
 
   Malacrida L, Jameson D, and Gratton E.
-  A multidimensional phasor approach reveals LAURDAN photophysics in NIH-3T3
-  cell membranes.
-  *Sci Rep*, 7: 9215 (2017). https://doi.org/10.1038/s41598-017-08564-z
+  `A multidimensional phasor approach reveals LAURDAN photophysics in NIH-3T3
+  cell membranes <https://doi.org/10.1038/s41598-017-08564-z>`_.
+  *Sci Rep*, 7: 9215 (2017).
 
 The dataset is available at https://zenodo.org/records/16894639.
 
@@ -73,12 +73,12 @@
 reference_signal = numpy.stack(
     [
         signal_from_fbd(
-            fetch(f'cumarinech{ch}_780LAURDAN_000$CC0Z.fbd'),
+            fetch(f'cumarinech{ch + 1}_780LAURDAN_000$CC0Z.fbd'),
             frame=-1,
-            channel=ch - 1,
+            channel=ch,
             laser_factor=0.99168,
         )
-        for ch in (1, 2)
+        for ch in (0, 1)
     ],
 )
 
@@ -141,15 +141,20 @@
     xlim=(-0.5, 1.05),
     ylim=(-0.1, 1.05),
     allquadrants=True,
-    title='Spectral phasor coordinates',
+    title='Spectral phasor',
 )
 
 # %%
 # Plot the FLIM phasor coordinates of channel 1 (blue) and 2 (orange):
 
-plot = PhasorPlot(frequency=frequency, title='FLIM phasor coordinates')
-plot.hist2d(flim_real[0], flim_imag[0], cmap='Blues')
-plot.hist2d(flim_real[1], flim_imag[1], cmap='Oranges', alpha=0.5)
+fig, axs = pyplot.subplots(2, 1, figsize=(6.4, 9))
+fig.suptitle('FLIM phasor')
+for ch in (0, 1):
+    plot = PhasorPlot(
+        ax=axs[ch], frequency=frequency, title=f'Channel {ch + 1}'
+    )
+    plot.hist2d(flim_real[ch], flim_imag[ch])
+fig.tight_layout()
 plot.show()
 
 # %%
@@ -166,7 +171,7 @@
 spectral_cursor_imag = [0.74, 0.70, 0.72]
 spectral_cursor_radius = 0.05
 
-spectral_masks = mask_from_circular_cursor(
+spectral_mask = mask_from_circular_cursor(
     spectral_real,
     spectral_imag,
     spectral_cursor_real,
@@ -196,7 +201,6 @@
     spectral_cursor_imag,
     radius=spectral_cursor_radius,
     color=CATEGORICAL[:3],
-    linestyle='-',
     linewidth=2,
 )
 plot.show()
@@ -208,7 +212,7 @@
 # Create a pseudo-colored image where each pixel is colored according to
 # its spectral classification based on the main cursors:
 
-spectral_pseudo_color = pseudo_color(*spectral_masks)
+spectral_pseudo_color = pseudo_color(*spectral_mask)
 plot_image(spectral_pseudo_color, title='Spectral pseudo-color image')
 
 # %%
@@ -221,17 +225,17 @@
 # the FLIM phasor space and reveals the correlation between spectral and
 # lifetime characteristics:
 
-fig, axs = pyplot.subplots(2, 1, figsize=(6.4, 8))
+fig, axs = pyplot.subplots(2, 1, figsize=(6.4, 9))
 fig.suptitle('Spectral classified FLIM phasor')
 
-for ch in range(2):
+for ch in (0, 1):
     plot = PhasorPlot(
         ax=axs[ch], frequency=frequency, title=f'Channel {ch + 1}'
     )
-    for i in range(spectral_masks.shape[0]):
+    for i in range(spectral_mask.shape[0]):
         plot.plot(
-            flim_real[ch][spectral_masks[i]],
-            flim_imag[ch][spectral_masks[i]],
+            flim_real[ch][spectral_mask[i]],
+            flim_imag[ch][spectral_mask[i]],
             color=CATEGORICAL[i],
             alpha=0.05,
             markersize=0.5,
@@ -253,7 +257,7 @@
 flim_cursor_imag = [0.44, 0.38, 0.41]
 flim_cursor_radius = 0.05
 
-flim_ch1_masks = mask_from_circular_cursor(
+flim_mask = mask_from_circular_cursor(
     flim_real[0],
     flim_imag[0],
     flim_cursor_real,
@@ -274,7 +278,6 @@
     flim_cursor_imag,
     radius=flim_cursor_radius,
     color=CATEGORICAL[:3],
-    linestyle='-',
     linewidth=2,
 )
 plot.show()
@@ -283,8 +286,7 @@
 # Display the FLIM pseudo-color image:
 
 plot_image(
-    pseudo_color(*flim_ch1_masks),
-    title='FLIM pseudo-color image (first channel)',
+    pseudo_color(*flim_mask), title='FLIM pseudo-color image (first channel)'
 )
 
 # %%
@@ -297,10 +299,10 @@
     title='FLIM classified spectral phasor',
 )
 
-for i in range(flim_ch1_masks.shape[0]):
+for i in range(flim_mask.shape[0]):
     plot.plot(
-        spectral_real[flim_ch1_masks[i]],
-        spectral_imag[flim_ch1_masks[i]],
+        spectral_real[flim_mask[i]],
+        spectral_imag[flim_mask[i]],
         color=CATEGORICAL[i],
         alpha=0.05,
         markersize=1,

docs/main/_downloads/22646849928d9a25253a25bacdb0e641/phasorpy_component.zip

@@ -16,7 +16,8 @@
 containing five fluorescent markers as presented in:
 
   Vallmitjana A, Lepanto P, Irigoin F, and Malacrida L.
-  Phasor-based multi-harmonic unmixing for in-vivo hyperspectral imaging.
+  `Phasor-based multi-harmonic unmixing for in-vivo hyperspectral imaging
+  <https://doi.org/10.1088/2050-6120/ac9ae9>`_.
   *Methods Appl Fluoresc*, 11(1): 014001 (2022).
 
 The dataset is available at https://zenodo.org/records/13625087.

docs/main/_downloads/2fdf8402d94927e02aeafb7456aacdc7/phasorpy_logo.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Multi-component fit\n\nSpectral unmixing using multi-component analysis in phasor space.\n\nFluorescent components in a fluorescence spectrum can be unmixed\nif the spectra of the individual components are known.\nThis can be achieved by solving a system of linear equations that fits\nthe fractional contributions of the phasor coordinates of the component\nspectra to the phasor coordinates of the mixture spectrum.\nPhasor coordinates at multiple harmonics may be used to ensure the\nlinear system is not underdetermined.\n\nThis analysis method is demonstrated using a hyperspectral imaging dataset\ncontaining five fluorescent markers as presented in:\n\n  Vallmitjana A, Lepanto P, Irigoin F, and Malacrida L.\n  Phasor-based multi-harmonic unmixing for in-vivo hyperspectral imaging.\n  *Methods Appl Fluoresc*, 11(1): 014001 (2022).\n\nThe dataset is available at https://zenodo.org/records/13625087.\n\nThe spectral unmixing of the five components is performed using phasor\ncoordinates at two harmonics.\n"
+        "\n# Multi-component fit\n\nSpectral unmixing using multi-component analysis in phasor space.\n\nFluorescent components in a fluorescence spectrum can be unmixed\nif the spectra of the individual components are known.\nThis can be achieved by solving a system of linear equations that fits\nthe fractional contributions of the phasor coordinates of the component\nspectra to the phasor coordinates of the mixture spectrum.\nPhasor coordinates at multiple harmonics may be used to ensure the\nlinear system is not underdetermined.\n\nThis analysis method is demonstrated using a hyperspectral imaging dataset\ncontaining five fluorescent markers as presented in:\n\n  Vallmitjana A, Lepanto P, Irigoin F, and Malacrida L.\n  [Phasor-based multi-harmonic unmixing for in-vivo hyperspectral imaging](https://doi.org/10.1088/2050-6120/ac9ae9).\n  *Methods Appl Fluoresc*, 11(1): 014001 (2022).\n\nThe dataset is available at https://zenodo.org/records/13625087.\n\nThe spectral unmixing of the five components is performed using phasor\ncoordinates at two harmonics.\n"
       ]
     },
     {