Merge pull request #158 from scijava/scijava/docs/media-scijava-org

Update links to use media.scijava.org

Author: gselzer

docs/ops/doc/Paper2024.rst

@@ -9,25 +9,25 @@ Python (scyjava)
 This use case illustrates the ease with which SciJava Ops can be accessed in Python, showcasing ``OpEnvironment`` setup and simple image processing. The full workflow can be found in the `scyjava use case <examples/scyjava.html>`_.
 
 +--------------+--------------------------------------------------------------------------------------+
-| |scyj_thumb| |`3D 3T3 Mouse Nucleus <https://media.imagej.net/scijava-ops/1.0.0/3t3_nucleus.tif>`_  |
+| |scyj_thumb| |`3D 3T3 Mouse Nucleus <https://media.scijava.org/scijava-ops/1.0.0/3t3_nucleus.tif>`_ |
 +--------------+--------------------------------------------------------------------------------------+
 
 Fluorescence Lifetime Image Analysis
 ------------------------------------
 
 This use case illustrates how SciJava Ops can be freely extended with additional algorithms libraries, making use of the SciJava framework for convenience and performance in FLIM analysis. The full workflow can be found in the `FLIM use case <examples/flim_analysis.html>`_.
 
-+--------------+----------------------------------------------------------------------------------+
-| |flim_thumb| | `BPAE cells <https://media.imagej.net/scijava-ops/1.0.0/flim_example_data.sdt>`_ |
-+--------------+----------------------------------------------------------------------------------+
++--------------+-----------------------------------------------------------------------------------+
+| |flim_thumb| | `BPAE cells <https://media.scijava.org/scijava-ops/1.0.0/flim_example_data.sdt>`_ |
++--------------+-----------------------------------------------------------------------------------+
 
 Spatially Adapted Colocalization Analysis
 -----------------------------------------
 
 This use case illustrates the novel scientific utility of the SciJava Ops Image library using powerful algorithms for pixel colocalization. The full workflow can be found in the `SACA use case <examples/deconvolution.html>`_.
 
 +--------------+----------------------------------------------------------------------------------------------------------------------+
-| |saca_thumb| | `HeLa cell expressing HIV gene products <https://media.imagej.net/scijava-ops/1.0.0/hela_hiv_gag_ms2_mcherry.tif>`_  |
+| |saca_thumb| | `HeLa cell expressing HIV gene products <https://media.scijava.org/scijava-ops/1.0.0/hela_hiv_gag_ms2_mcherry.tif>`_ |
 +--------------+----------------------------------------------------------------------------------------------------------------------+
 
 Deconvolution
@@ -36,15 +36,15 @@ Deconvolution
 This use case illustrates the novel scientific utility of the SciJava Ops Image library using powerful algorithms for image deconvolution. The full workflow can be found in the `deconvolution use case <examples/deconvolution.html>`_.
 
 +---------------+-------------------------------------------------------------------------------------------+
-| |decon_thumb| | `3D HeLa nucleus <https://media.imagej.net/scijava-ops/1.0.0/hela_nucleus.tif>`_          |
+| |decon_thumb| | `3D HeLa nucleus <https://media.scijava.org/scijava-ops/1.0.0/hela_nucleus.tif>`_         |
 +---------------+-------------------------------------------------------------------------------------------+
 
-.. |decon_thumb| image:: https://media.imagej.net/scijava-ops/1.0.0/hela_nucleus_thumbnail.png
+.. |decon_thumb| image:: https://media.scijava.org/scijava-ops/1.0.0/hela_nucleus_thumbnail.png
     :width: 10em
-.. |flim_thumb| image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_input_56.png
+.. |flim_thumb| image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_input_56.png
     :width: 10em
-.. |saca_thumb| image:: https://media.imagej.net/scijava-ops/1.0.0/hela_hiv_gag_ms2_mcherry_thumbnail.png
+.. |saca_thumb| image:: https://media.scijava.org/scijava-ops/1.0.0/hela_hiv_gag_ms2_mcherry_thumbnail.png
     :width: 10em
-.. |scyj_thumb| image:: https://media.imagej.net/scijava-ops/1.0.0/3t3_nucleus_thumbnail.png
+.. |scyj_thumb| image:: https://media.scijava.org/scijava-ops/1.0.0/3t3_nucleus_thumbnail.png
     :width: 10em
 

docs/ops/doc/examples/deconvolution.rst

@@ -10,7 +10,7 @@ the RLTV algorithm returns improved axial and lateral resolution when compared t
 
 You can download the 3D HeLa cell nuclus dataset `here`_.
 
-.. figure:: https://media.imagej.net/scijava-ops/1.0.0/rltv_example_1.gif
+.. figure:: https://media.scijava.org/scijava-ops/1.0.0/rltv_example_1.gif
 
     Results of RLTV deconvolution on the sample data.
 
@@ -145,5 +145,5 @@ SciJava Ops via Fiji's scripting engine with `script parameters`_:
 
 .. _`Dey et. al, Micros Res Tech 2006`: https://pubmed.ncbi.nlm.nih.gov/16586486/
 .. _`Gibson & Lanni, JOSA 1992`: https://pubmed.ncbi.nlm.nih.gov/1738047/
-.. _`here`: https://media.imagej.net/scijava-ops/1.0.0/hela_nucleus.tif
+.. _`here`: https://media.scijava.org/scijava-ops/1.0.0/hela_nucleus.tif
 .. _`script parameters`: https://imagej.net/scripting/parameters

docs/ops/doc/examples/flim_analysis.rst

@@ -4,16 +4,16 @@ FLIM Analysis
 
 In this example we will use SciJava Ops within Fiji to perform `FLIM`_ analysis, which is used in many situations including photosensitizer detection and `FRET`_ measurement.
 
-.. image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_input.gif
+.. image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_input.gif
     :width: 49%
-.. image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_pseudocolored_annotated.png
+.. image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_pseudocolored_annotated.png
     :width: 49%
 
 We use a sample of `FluoCells™ Prepared Slide #1`_, imaged by `Jenu Chacko`_ using `Openscan-LSM`_ and SPC180 electronics with multiphoton excitation and a 40x WI lens. **Notably, the full field for this image is 130 microns in each axial dimension**.
 
   FluoCells™ Prepared Slide #1 contains bovine pulmonary artery endothelial cells (BPAEC). MitoTracker™ Red CMXRos was used to stain the mitochondria in the live cells, with accumulation dependent upon membrane potential. Following fixation and permeabilization, F-actin was stained with Alexa Fluor™ 488 phalloidin, and the nuclei were counterstained with the blue-fluorescent DNA stain DAPI.
 
-The sample data can be downloaded `here <https://media.imagej.net/scijava-ops/1.0.0/flim_example_data.sdt>`_ and can be loaded into Fiji with `Bio-Formats`_ using ``File → Open``. When presented with the ``Bio-Formats Import Options`` screen, it may be helpful to select ``Metadata viewing → Display metadata`` to determine values necessary for analysis. Then, select ``OK``. The data may take a minute to load.
+The sample data can be downloaded `here <https://media.scijava.org/scijava-ops/1.0.0/flim_example_data.sdt>`_ and can be loaded into Fiji with `Bio-Formats`_ using ``File → Open``. When presented with the ``Bio-Formats Import Options`` screen, it may be helpful to select ``Metadata viewing → Display metadata`` to determine values necessary for analysis. Then, select ``OK``. The data may take a minute to load.
 
 Within the script, the `Levenberg-Marquardt algorithm`_ fitting Op of SciJava Ops FLIM is used to fit the data.
 
@@ -48,13 +48,13 @@ The script above will display the fit results, as well as a *pseudocolored* outp
 
   * Value is a function of A\ :subscript:`1`
 
-.. image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_a1.png
+.. image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_a1.png
     :width: 32%
 
-.. image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_tau1.png
+.. image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_tau1.png
     :width: 32%
 
-.. image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_pseudocolored.png
+.. image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_pseudocolored.png
     :width: 32%
 
 
@@ -168,10 +168,10 @@ With 130 microns across each direction in the dataset, we have 512/130~3.938 pix
 
 The results are shown in the panels below. The left panel shows panel 56 of the original image, contrasted using ImageJ's Brightness and Contrast tool, and the right panel shows the **annotated**, pseudocolored results.
 
-.. image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_input_56.png
+.. image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_input_56.png
     :width: 49%
 
-.. image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_pseudocolored_annotated.png
+.. image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_pseudocolored_annotated.png
     :width: 49%
 
 .. tabs::
@@ -215,10 +215,10 @@ The provided script allows users to specify ROIs by drawing selections using the
 
 In the panels below, we show the results of executing both scripts with computation restricted to the area around a single cell. The left panel shows slide 56 of the input data, annotated with an elliptical ROI drawn using ImageJ's elliptical selection tool and contrasted using ImageJ's Brightness and Contrast tool. The right panel shows the pseudocolored result, annotated with color and scale bars, with computation limited to the selected ellipse.
 
-.. image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_input_56_roi.png
+.. image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_input_56_roi.png
     :width: 49%
 
-.. image:: https://media.imagej.net/scijava-ops/1.0.0/flim_example_pseudocolored_annotated_roi.png
+.. image:: https://media.scijava.org/scijava-ops/1.0.0/flim_example_pseudocolored_annotated_roi.png
     :width: 49%
 
 

docs/ops/doc/examples/opencv_denoise.rst

@@ -11,7 +11,7 @@ images for comparison.
 
 The sample data for this example can be downloaded `here`_.
 
-.. figure:: https://media.imagej.net/scijava-ops/1.0.0/opencv_denoise_example_1.png
+.. figure:: https://media.scijava.org/scijava-ops/1.0.0/opencv_denoise_example_1.png
 
     Results of OpenCV's non-local means denoise algorithm with the sample data.
 
@@ -121,4 +121,4 @@ SciJava Ops via Fiji's scripting engine with `script parameters`_:
 
 .. _`script parameters`: https://imagej.net/scripting/parameters
 .. _`OpenCV libray`: https://docs.opencv.org/4.x/d5/d69/tutorial_py_non_local_means.html
-.. _`here`: https://media.imagej.net/scijava-ops/1.0.0/opencv_denoise_16bit.tif
+.. _`here`: https://media.scijava.org/scijava-ops/1.0.0/opencv_denoise_16bit.tif

docs/ops/doc/examples/scyjava.rst

@@ -8,7 +8,7 @@ nucleus dataset (with shape: ``37, 300, 300``), performes image processing with
 the 3D volume of the nucleus by creating a mesh. Finally the input image, processed image and the segmented label images are displayed in
 ``matplotlib``, and the volume (μm\ :sup:`3`) is printed to the console.
 
-.. figure:: https://media.imagej.net/scijava-ops/1.0.0/scyjava_example_1.png
+.. figure:: https://media.scijava.org/scijava-ops/1.0.0/scyjava_example_1.png
 
 .. code-block:: bash
 
@@ -148,7 +148,7 @@ Activate the ``scijava-ops`` conda/mamba environment and run the following Pytho
     ops = OpEnvironment.build()
 
     # open image
-    narr = read_image_from_url("https://media.imagej.net/scijava-ops/1.0.0/3t3_nucleus.tif")
+    narr = read_image_from_url("https://media.scijava.org/scijava-ops/1.0.0/3t3_nucleus.tif")
     cal = [0.065, 0.065, 0.1] # microns, from imaging parameters
     rai = numpy_to_imglib(narr)
     results = segment_nuclei(rai)
@@ -167,4 +167,4 @@ Activate the ``scijava-ops`` conda/mamba environment and run the following Pytho
     plt.tight_layout()
     plt.show()
 
-.. _`3D 3T3 cell`: https://media.imagej.net/scijava-ops/1.0.0/3t3_nucleus.tif
+.. _`3D 3T3 cell`: https://media.scijava.org/scijava-ops/1.0.0/3t3_nucleus.tif