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9e6fa4dd638..83477ec37be 100644 --- a/dev/_sources/auto_examples/00_tutorials/plot_3d_and_4d_niimg.rst.txt +++ b/dev/_sources/auto_examples/00_tutorials/plot_3d_and_4d_niimg.rst.txt @@ -125,7 +125,7 @@ statistical map: .. code-block:: none - + @@ -154,7 +154,7 @@ Visualizing works better with a threshold .. code-block:: none - + @@ -263,7 +263,7 @@ We can then plot it .. code-block:: none - + @@ -474,7 +474,7 @@ image. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 16.097 seconds) + **Total running time of the script:** (0 minutes 19.974 seconds) **Estimated memory usage:** 127 MB diff --git a/dev/_sources/auto_examples/00_tutorials/plot_decoding_tutorial.rst.txt b/dev/_sources/auto_examples/00_tutorials/plot_decoding_tutorial.rst.txt index 4d0b3db1dfd..92106db17fc 100644 --- a/dev/_sources/auto_examples/00_tutorials/plot_decoding_tutorial.rst.txt +++ b/dev/_sources/auto_examples/00_tutorials/plot_decoding_tutorial.rst.txt @@ -197,7 +197,7 @@ Haxby 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2.60399830e-02 1.50401307e-02 1.27425133e-02 -2.29243926e-02 + -1.06664941e-02 9.81930805e-03 -4.77511796e-02 1.64244040e-02]] @@ -1002,7 +1002,7 @@ We can plot the weights, using the subject's anatomical as a background $( window ).on('load',function() { // Create brain slices var brain = brainsprite( - {"canvas": "3Dviewer", "sprite": "spriteImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "overlay": {"sprite": "overlayImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "opacity": 1}, "colorBackground": "#000000", "colorFont": "#FFFFFF", "crosshair": true, "affine": [[1.2000000476837158, 0.0, 0.0, -73.80000293254852], [0.0, 0.9375, 0.0, -119.53125], [0.0, 0.0, 0.9375, -119.53125], [0.0, 0.0, 0.0, 1.0]], "flagCoordinates": true, "title": "SVM weights", "flagValue": false, "numSlice": {"X": 50.8869827506137, "Y": 83.14770060746312, "Z": 117.91566820866842}, "colorMap": {"img": "colorMap", "min": -0.17588914930820465, "max": 0.17588914930820465}} + {"canvas": "3Dviewer", "sprite": "spriteImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "overlay": {"sprite": "overlayImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "opacity": 1}, "colorBackground": "#000000", "colorFont": "#FFFFFF", "crosshair": true, "affine": [[1.2000000476837158, 0.0, 0.0, -73.80000293254852], [0.0, 0.9375, 0.0, -119.53125], [0.0, 0.0, 0.9375, -119.53125], [0.0, 0.0, 0.0, 1.0]], "flagCoordinates": true, "title": "SVM weights", "flagValue": false, "numSlice": {"X": 50.88699175671044, "Y": 83.14770511513618, "Z": 117.91567701865729}, "colorMap": {"img": "colorMap", "min": -0.17588911950588226, "max": 0.17588911950588226}} ); }); </script> @@ -1077,7 +1077,7 @@ ______________ .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 39.634 seconds) + **Total running time of the script:** (0 minutes 43.816 seconds) **Estimated memory usage:** 916 MB diff --git a/dev/_sources/auto_examples/00_tutorials/plot_nilearn_101.rst.txt b/dev/_sources/auto_examples/00_tutorials/plot_nilearn_101.rst.txt index 4b37f8cbe64..ddb17369ac1 100644 --- a/dev/_sources/auto_examples/00_tutorials/plot_nilearn_101.rst.txt +++ b/dev/_sources/auto_examples/00_tutorials/plot_nilearn_101.rst.txt @@ -77,7 +77,7 @@ Let's quickly plot this file: .. code-block:: none - + @@ -125,7 +125,7 @@ Here we give as inputs the image filename and the smoothing value in mm .. code-block:: none - + @@ -154,7 +154,7 @@ instance to look at it .. code-block:: none - + @@ -183,7 +183,7 @@ We could also pass it to the smoothing function .. code-block:: none - + @@ -238,9 +238,9 @@ passed on to other nilearn tools, or saved to disk. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 6.310 seconds) + **Total running time of the script:** (0 minutes 6.202 seconds) -**Estimated memory usage:** 265 MB +**Estimated memory usage:** 255 MB .. _sphx_glr_download_auto_examples_00_tutorials_plot_nilearn_101.py: diff --git a/dev/_sources/auto_examples/00_tutorials/plot_python_101.rst.txt b/dev/_sources/auto_examples/00_tutorials/plot_python_101.rst.txt index 240a601db64..b9430d852ea 100644 --- a/dev/_sources/auto_examples/00_tutorials/plot_python_101.rst.txt +++ b/dev/_sources/auto_examples/00_tutorials/plot_python_101.rst.txt @@ -53,14 +53,14 @@ A simple example of basic Python numerics and how to plot it. .. code-block:: none - [] + [] .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.714 seconds) + **Total running time of the script:** (0 minutes 0.855 seconds) **Estimated memory usage:** 10 MB diff --git a/dev/_sources/auto_examples/00_tutorials/plot_single_subject_single_run.rst.txt b/dev/_sources/auto_examples/00_tutorials/plot_single_subject_single_run.rst.txt index 46dfb12cfdb..664d684ea2f 100644 --- a/dev/_sources/auto_examples/00_tutorials/plot_single_subject_single_run.rst.txt +++ b/dev/_sources/auto_examples/00_tutorials/plot_single_subject_single_run.rst.txt @@ -128,7 +128,7 @@ We can display the first functional image and the subject's anatomy: .. code-block:: none - + @@ -1261,7 +1261,7 @@ Oops, there is a lot of non-neural signal in there (ventricles, arteries)... .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 16.349 seconds) + **Total running time of the script:** (0 minutes 21.181 seconds) **Estimated memory usage:** 275 MB diff --git a/dev/_sources/auto_examples/00_tutorials/sg_execution_times.rst.txt b/dev/_sources/auto_examples/00_tutorials/sg_execution_times.rst.txt index ace87ad1fb6..f61e5b9b9b7 100644 --- a/dev/_sources/auto_examples/00_tutorials/sg_execution_times.rst.txt +++ b/dev/_sources/auto_examples/00_tutorials/sg_execution_times.rst.txt @@ -6,16 +6,16 @@ Computation times ================= -**01:19.105** total execution time for **auto_examples_00_tutorials** files: +**01:32.027** total execution time for **auto_examples_00_tutorials** files: +----------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_00_tutorials_plot_decoding_tutorial.py` (``plot_decoding_tutorial.py``) | 00:39.634 | 916.4 MB | +| :ref:`sphx_glr_auto_examples_00_tutorials_plot_decoding_tutorial.py` (``plot_decoding_tutorial.py``) | 00:43.816 | 916.3 MB | +----------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_00_tutorials_plot_single_subject_single_run.py` (``plot_single_subject_single_run.py``) | 00:16.349 | 275.1 MB | +| :ref:`sphx_glr_auto_examples_00_tutorials_plot_single_subject_single_run.py` (``plot_single_subject_single_run.py``) | 00:21.181 | 275.1 MB | +----------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_00_tutorials_plot_3d_and_4d_niimg.py` (``plot_3d_and_4d_niimg.py``) | 00:16.097 | 127.3 MB | +| :ref:`sphx_glr_auto_examples_00_tutorials_plot_3d_and_4d_niimg.py` (``plot_3d_and_4d_niimg.py``) | 00:19.974 | 127.4 MB | +----------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_00_tutorials_plot_nilearn_101.py` (``plot_nilearn_101.py``) | 00:06.310 | 265.4 MB | +| :ref:`sphx_glr_auto_examples_00_tutorials_plot_nilearn_101.py` (``plot_nilearn_101.py``) | 00:06.202 | 255.0 MB | +----------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_00_tutorials_plot_python_101.py` (``plot_python_101.py``) | 00:00.714 | 9.5 MB | +| :ref:`sphx_glr_auto_examples_00_tutorials_plot_python_101.py` (``plot_python_101.py``) | 00:00.855 | 9.6 MB | +----------------------------------------------------------------------------------------------------------------------+-----------+----------+ diff --git a/dev/_sources/auto_examples/01_plotting/plot_3d_map_to_surface_projection.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_3d_map_to_surface_projection.rst.txt index 05a09e04368..c55feb6c611 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_3d_map_to_surface_projection.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_3d_map_to_surface_projection.rst.txt @@ -199,7 +199,7 @@ of ``matplotlib``:
-
+


@@ -272,7 +272,7 @@ Plot 3D image for comparison .. code-block:: none - + @@ -1465,9 +1465,9 @@ interpolation with zero radius will achieve this. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 41.950 seconds) + **Total running time of the script:** (0 minutes 35.803 seconds) -**Estimated memory usage:** 424 MB +**Estimated memory usage:** 460 MB .. _sphx_glr_download_auto_examples_01_plotting_plot_3d_map_to_surface_projection.py: diff --git a/dev/_sources/auto_examples/01_plotting/plot_atlas.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_atlas.rst.txt index cb47cfa522b..e639901a531 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_atlas.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_atlas.rst.txt @@ -86,7 +86,7 @@ Visualizing the Harvard-Oxford atlas .. code-block:: none - + @@ -116,7 +116,7 @@ Visualizing the Juelich atlas .. code-block:: none - + @@ -184,9 +184,9 @@ Visualizing the Juelich atlas with contours .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 22.608 seconds) + **Total running time of the script:** (1 minutes 46.368 seconds) -**Estimated memory usage:** 443 MB +**Estimated memory usage:** 457 MB .. _sphx_glr_download_auto_examples_01_plotting_plot_atlas.py: diff --git a/dev/_sources/auto_examples/01_plotting/plot_carpet.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_carpet.rst.txt index 239f2f415c4..1e222f1e635 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_carpet.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_carpet.rst.txt @@ -204,9 +204,9 @@ Visualizing global patterns, separated by tissue type .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 9.327 seconds) + **Total running time of the script:** (0 minutes 11.000 seconds) -**Estimated memory usage:** 913 MB +**Estimated memory usage:** 963 MB .. _sphx_glr_download_auto_examples_01_plotting_plot_carpet.py: diff --git a/dev/_sources/auto_examples/01_plotting/plot_colormaps.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_colormaps.rst.txt index 4a703075254..261490970a6 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_colormaps.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_colormaps.rst.txt @@ -121,9 +121,9 @@ Plot matplotlib color maps .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.251 seconds) + **Total running time of the script:** (0 minutes 3.692 seconds) -**Estimated memory usage:** 18 MB +**Estimated memory usage:** 19 MB .. _sphx_glr_download_auto_examples_01_plotting_plot_colormaps.py: diff --git a/dev/_sources/auto_examples/01_plotting/plot_demo_glass_brain.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_demo_glass_brain.rst.txt index c48156e1d46..7538f7ed953 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_demo_glass_brain.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_demo_glass_brain.rst.txt @@ -72,7 +72,7 @@ Glass brain plotting: whole brain sagittal cuts .. code-block:: none - + @@ -109,7 +109,7 @@ the z view (option "display_mode"). .. code-block:: none - + @@ -146,7 +146,7 @@ Glass brain plotting: Hemispheric sagittal cuts .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.331 seconds) + **Total running time of the script:** (0 minutes 4.101 seconds) **Estimated memory usage:** 18 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_demo_glass_brain_extensive.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_demo_glass_brain_extensive.rst.txt index 5e2f1967b56..5e2c7022f8c 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_demo_glass_brain_extensive.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_demo_glass_brain_extensive.rst.txt @@ -104,7 +104,7 @@ a :class:`~nilearn.plotting.displays.OrthoProjector` is returned. .. code-block:: none - + @@ -135,7 +135,7 @@ setting ``colorbar=True``. .. code-block:: none - + @@ -173,7 +173,7 @@ axial projections by setting ``display_mode='xz'``, which returns a .. code-block:: none - + @@ -208,7 +208,7 @@ Additionally, we only visualize coronal and axial projections by setting .. code-block:: none - + @@ -240,7 +240,7 @@ Setting ``plot_abs=True`` and ``display_mode='yx'`` (returns a .. code-block:: none - + @@ -281,7 +281,7 @@ colorbar will not be centered around zero. .. code-block:: none - + @@ -319,7 +319,7 @@ losing colours due to the thresholding. .. code-block:: none - + @@ -363,7 +363,7 @@ enables an hemispheric sagittal view. The display object returned is then a .. code-block:: none - + @@ -401,7 +401,7 @@ enables an hemispheric sagittal view. The display object returned is then a .. code-block:: none - + @@ -444,7 +444,7 @@ If you are only interested in single projections, you can set .. code-block:: none - + @@ -828,7 +828,7 @@ Next, using the same display object, we plot positive sign of activation. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 26.643 seconds) + **Total running time of the script:** (0 minutes 33.498 seconds) **Estimated memory usage:** 9 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_demo_more_plotting.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_demo_more_plotting.rst.txt index 86cc5fd3e2f..a1d1b34bef8 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_demo_more_plotting.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_demo_more_plotting.rst.txt @@ -145,7 +145,7 @@ By default the ``colorbar`` argument is set to ``True`` in .. code-block:: none - + @@ -188,7 +188,7 @@ automatically. .. code-block:: none - + @@ -228,7 +228,7 @@ integers. .. code-block:: none - + @@ -270,7 +270,7 @@ again, selected automatically. .. code-block:: none - + @@ -310,7 +310,7 @@ a colorbar on the right side. .. code-block:: none - + @@ -353,7 +353,7 @@ slices to be displayed. .. code-block:: none - + @@ -393,7 +393,7 @@ The coordinates will be assigned in the order of direction as [x, y, z]. .. code-block:: none - + @@ -432,7 +432,7 @@ object. .. code-block:: none - + @@ -472,7 +472,7 @@ a :class:`~nilearn.plotting.displays.TiledSlicer` object. .. code-block:: none - + @@ -512,7 +512,7 @@ In addition, we show here the default option ``cut_coords=None``. .. code-block:: none - + @@ -551,7 +551,7 @@ an integer, i.e. ``cut_coords=3``. .. code-block:: none - + @@ -590,7 +590,7 @@ we specify the number of slices as a tuple of length 3. .. code-block:: none - + @@ -906,7 +906,7 @@ object returned. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 27.563 seconds) + **Total running time of the script:** (0 minutes 33.935 seconds) **Estimated memory usage:** 916 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_demo_plotting.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_demo_plotting.rst.txt index 1fc9460d324..0a41291f2d0 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_demo_plotting.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_demo_plotting.rst.txt @@ -130,7 +130,7 @@ Plotting statistical maps with function `plot_stat_map` .. code-block:: none - + @@ -256,7 +256,7 @@ brain is always a fixed background template .. code-block:: none - + @@ -287,7 +287,7 @@ Visualizing anatomical image of haxby dataset .. code-block:: none - + @@ -322,7 +322,7 @@ with coordinates positioned automatically on region of interest (roi) .. code-block:: none - + @@ -360,7 +360,7 @@ Plotting EPI image with function `plot_epi` .. code-block:: none - + @@ -385,7 +385,7 @@ in script mode (ie outside IPython) .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 13.158 seconds) + **Total running time of the script:** (0 minutes 15.087 seconds) **Estimated memory usage:** 916 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_dim_plotting.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_dim_plotting.rst.txt index 99da3e2a7e6..6e30e95a15b 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_dim_plotting.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_dim_plotting.rst.txt @@ -90,7 +90,7 @@ Plotting with enhancement of background image with dim=-.5 .. code-block:: none - + @@ -126,7 +126,7 @@ Plotting with no change of contrast in background image with dim=0 .. code-block:: none - + @@ -162,7 +162,7 @@ Plotting with decrease of contrast in background image with dim=.5 .. code-block:: none - + @@ -200,7 +200,7 @@ Plotting with more decrease in contrast with dim=1 .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 4.797 seconds) + **Total running time of the script:** (0 minutes 6.072 seconds) **Estimated memory usage:** 9 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_haxby_masks.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_haxby_masks.rst.txt index 598a9ec37af..9186c785d43 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_haxby_masks.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_haxby_masks.rst.txt @@ -134,7 +134,7 @@ Plot the masks .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 5.657 seconds) + **Total running time of the script:** (0 minutes 6.038 seconds) **Estimated memory usage:** 916 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_multiscale_parcellations.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_multiscale_parcellations.rst.txt index 76efb3fd163..4007bda9ddc 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_multiscale_parcellations.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_multiscale_parcellations.rst.txt @@ -123,7 +123,7 @@ Visualizing brain parcellations .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.516 seconds) + **Total running time of the script:** (0 minutes 3.407 seconds) **Estimated memory usage:** 9 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_overlay.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_overlay.rst.txt index 759523317eb..bd1f7c133a7 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_overlay.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_overlay.rst.txt @@ -163,7 +163,7 @@ Unlike :func:`nilearn.plotting.plot_stat_map` this works with 4D images .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.206 seconds) + **Total running time of the script:** (0 minutes 4.435 seconds) **Estimated memory usage:** 11 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_prob_atlas.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_prob_atlas.rst.txt index 84c7f2a18ab..c303530e176 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_prob_atlas.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_prob_atlas.rst.txt @@ -196,7 +196,7 @@ locally to get the same plots as above for each of the listed atlases. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 20.888 seconds) + **Total running time of the script:** (0 minutes 26.292 seconds) **Estimated memory usage:** 342 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_surf_atlas.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_surf_atlas.rst.txt index 3f964b40b48..46d75364f50 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_surf_atlas.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_surf_atlas.rst.txt @@ -1043,7 +1043,7 @@ interactive view of the connectome. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 7.249 seconds) + **Total running time of the script:** (0 minutes 8.314 seconds) **Estimated memory usage:** 35 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_surf_stat_map.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_surf_stat_map.rst.txt index 8a327847bd8..a0f629f3555 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_surf_stat_map.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_surf_stat_map.rst.txt @@ -404,7 +404,7 @@ creating the figure .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 18.180 seconds) + **Total running time of the script:** (0 minutes 20.477 seconds) **Estimated memory usage:** 302 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_surface_projection_strategies.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_surface_projection_strategies.rst.txt index c8477eb7b0c..6f7c583b80b 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_surface_projection_strategies.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_surface_projection_strategies.rst.txt @@ -205,7 +205,7 @@ position of samples along the line .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 1.106 seconds) + **Total running time of the script:** (0 minutes 1.413 seconds) **Estimated memory usage:** 10 MB diff --git a/dev/_sources/auto_examples/01_plotting/plot_visualization.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_visualization.rst.txt index 8764966121f..a1f47148fdb 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_visualization.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_visualization.rst.txt @@ -92,7 +92,7 @@ Visualization .. code-block:: none - + @@ -129,7 +129,7 @@ Simple computation of a mask from the fMRI data .. code-block:: none - + @@ -176,9 +176,9 @@ Applying the mask to extract the corresponding time series .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 20.527 seconds) + **Total running time of the script:** (0 minutes 19.933 seconds) -**Estimated memory usage:** 1354 MB +**Estimated memory usage:** 1364 MB .. _sphx_glr_download_auto_examples_01_plotting_plot_visualization.py: diff --git a/dev/_sources/auto_examples/01_plotting/plot_visualize_megatrawls_netmats.rst.txt b/dev/_sources/auto_examples/01_plotting/plot_visualize_megatrawls_netmats.rst.txt index 41179884d7c..cc22d1b3006 100644 --- a/dev/_sources/auto_examples/01_plotting/plot_visualize_megatrawls_netmats.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/plot_visualize_megatrawls_netmats.rst.txt @@ -87,7 +87,7 @@ correlation matrices .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.544 seconds) + **Total running time of the script:** (0 minutes 0.646 seconds) **Estimated memory usage:** 9 MB diff --git a/dev/_sources/auto_examples/01_plotting/sg_execution_times.rst.txt b/dev/_sources/auto_examples/01_plotting/sg_execution_times.rst.txt index a99bb1bfa21..813b74c0739 100644 --- a/dev/_sources/auto_examples/01_plotting/sg_execution_times.rst.txt +++ b/dev/_sources/auto_examples/01_plotting/sg_execution_times.rst.txt @@ -6,42 +6,42 @@ Computation times ================= -**04:52.499** total execution time for **auto_examples_01_plotting** files: +**05:40.510** total execution time for **auto_examples_01_plotting** files: -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_atlas.py` (``plot_atlas.py``) | 01:22.608 | 442.7 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_3d_map_to_surface_projection.py` (``plot_3d_map_to_surface_projection.py``) | 00:41.950 | 424.3 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_demo_more_plotting.py` (``plot_demo_more_plotting.py``) | 00:27.563 | 916.2 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_demo_glass_brain_extensive.py` (``plot_demo_glass_brain_extensive.py``) | 00:26.643 | 9.0 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_prob_atlas.py` (``plot_prob_atlas.py``) | 00:20.888 | 341.6 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_visualization.py` (``plot_visualization.py``) | 00:20.527 | 1353.9 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_surf_stat_map.py` (``plot_surf_stat_map.py``) | 00:18.180 | 301.8 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_demo_plotting.py` (``plot_demo_plotting.py``) | 00:13.158 | 916.4 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_carpet.py` (``plot_carpet.py``) | 00:09.327 | 913.0 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_surf_atlas.py` (``plot_surf_atlas.py``) | 00:07.249 | 34.6 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_haxby_masks.py` (``plot_haxby_masks.py``) | 00:05.657 | 916.4 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_dim_plotting.py` (``plot_dim_plotting.py``) | 00:04.797 | 9.2 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_demo_glass_brain.py` (``plot_demo_glass_brain.py``) | 00:03.331 | 17.6 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_colormaps.py` (``plot_colormaps.py``) | 00:03.251 | 18.5 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_overlay.py` (``plot_overlay.py``) | 00:03.206 | 10.8 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_multiscale_parcellations.py` (``plot_multiscale_parcellations.py``) | 00:02.516 | 9.0 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_surface_projection_strategies.py` (``plot_surface_projection_strategies.py``) | 00:01.106 | 9.8 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_01_plotting_plot_visualize_megatrawls_netmats.py` (``plot_visualize_megatrawls_netmats.py``) | 00:00.544 | 9.0 MB | -+-----------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_atlas.py` (``plot_atlas.py``) | 01:46.368 | 457.5 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_3d_map_to_surface_projection.py` (``plot_3d_map_to_surface_projection.py``) | 00:35.803 | 460.3 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_demo_more_plotting.py` (``plot_demo_more_plotting.py``) | 00:33.935 | 916.4 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_demo_glass_brain_extensive.py` (``plot_demo_glass_brain_extensive.py``) | 00:33.498 | 9.0 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_prob_atlas.py` (``plot_prob_atlas.py``) | 00:26.292 | 342.4 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_surf_stat_map.py` (``plot_surf_stat_map.py``) | 00:20.477 | 301.8 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_visualization.py` (``plot_visualization.py``) | 00:19.933 | 1363.7 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_demo_plotting.py` (``plot_demo_plotting.py``) | 00:15.087 | 916.4 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_carpet.py` (``plot_carpet.py``) | 00:10.1000 | 963.3 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_surf_atlas.py` (``plot_surf_atlas.py``) | 00:08.314 | 34.8 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_dim_plotting.py` (``plot_dim_plotting.py``) | 00:06.072 | 9.2 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_haxby_masks.py` (``plot_haxby_masks.py``) | 00:06.038 | 916.4 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_overlay.py` (``plot_overlay.py``) | 00:04.435 | 10.8 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_demo_glass_brain.py` (``plot_demo_glass_brain.py``) | 00:04.101 | 17.6 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_colormaps.py` (``plot_colormaps.py``) | 00:03.692 | 19.2 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_multiscale_parcellations.py` (``plot_multiscale_parcellations.py``) | 00:03.407 | 9.0 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_surface_projection_strategies.py` (``plot_surface_projection_strategies.py``) | 00:01.413 | 9.9 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ +| :ref:`sphx_glr_auto_examples_01_plotting_plot_visualize_megatrawls_netmats.py` (``plot_visualize_megatrawls_netmats.py``) | 00:00.646 | 9.0 MB | ++-----------------------------------------------------------------------------------------------------------------------------+------------+-----------+ diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_anova_svm.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_anova_svm.rst.txt index cff39a0eb96..00a6ab580d8 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_anova_svm.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_anova_svm.rst.txt @@ -286,7 +286,7 @@ dynamic html viewer <!-- the colormap --> <img id="colorMap" class="hidden" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQAAAAABCAYAAAAxWXB3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAAUUlEQVR4nGP8/////x8MDAz48E8ay//7w8DA8BuKfyGxqcGnhZkYdvyno2XU5fMyfGZgZWCAYzYk9mDnM7LS2XJqm8nCxMDAwIEHs9NYnoMBAFEChf4wrnHMAAAAAElFTkSuQmCC" alt="colormap"> <!-- another sprite image, with an overlay--> - <img 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F/Opf96ZkL07RtJL2Rr/s3xv5SJ/az1v70NAAAAAEA9uCoFAKBvwi23AQCoz4O6D6D5vhWZJOClzJD1Jznfs1FkGwKBZprl/gyCyD6WpAIeb7enUmTToReSXjb+HBpFye8nDT9eoLm+LUUX8e0bST/LHf/jSHoot9J63vIVQFm+L0UPZVowN5L+IXf8X0bSY5k2liT9lPgHGupjL/7vJP2kkPi/kKn/JekXxD9KFKSuB2ecbwUKpEiSZhrLtOs/5/1FgwRRcsII8V+kURz/1qTx/YPoF/qDyxRIkd30eCDpmvgHAAAAAKD1Aq+/L70Mz0yB6F9tNlYA3us8/icVPYMnDpBCH7NsQ5kF/UK1aT4TK4QBRbiQGdaXioomu3rvtJBHA1CeC5l03eKcyaUTUj8DTXYh6Z34djG7nJzI1f/EP8rW1r152iXQrWZm2w+gQQZbbgMA8vB72mnNAwAAAADQDbt6UOMFTtFgJADvVXRXlv9Y+QcgAkIMQEuNdK2BriWZknFS7+EAqNBIL3XqfX9d25EAqNpIr1erhUnEP6rAhNAy2Xg+14x4RsOQ9AsAZaN1BQAAAABAN3HN3y4kAO819W7f5X60kWaaKFBbByLm8del2hTs7TlSoMnuttw+1tflVhS+k/TTAh4TQDluttw+1p9LeldmHdCvJP2ggMcEUI6i6///Uab+P4kf70cFPCawnZ9ujrIMZGIaaB4mAFSBdxfop1Dt3OEQAAAAAACso4+v3R7UfQDtM44C3a5WrfPXB54pWK3IeyqzufWp3Cb3oaSXa+/5ZSQtNNZEj2UGwy/kNsX9acWf0ZUU2de2kPS6cedIEJmv9l2dNez4AOdSik7kEuZvGxdP9fp7KXo/vv1K0vd5f9AhgRT53884vxM+kaKvy9Tmv5H0Ee8POiVYxX+8JQ7nt+d/TtX/H/P+oFTjKJkAeFvp+XbptQfKuR4YRes/CzXSLNEn4SfnLmWu9efxVzdB2fVcBPHfd+/6JUi9X/QndNtl5M7+paTryvvXbFTNVVb8+xMcTPyONNNQ0rlcGeB+a/oa/TJgtioDwsQuY91rvxD//ZKuHyeV1/+2P9DUtfXV/7YcStf/LnnXsPEfJ/R2LD6If6A96m2/AajTVar/5jXxD/QG9T9gjeL+RGm1g3YqHtL9Afv68+z9V1mNHYsvrvd9rACcUaDbVceZpLXVfGcKNNJMA7kkYL/Df93J6tFOZZJ/L8o59IOcShrGt5u7TtIg9RVoppPUVyS9L+lrcgMSAPrjTyWd/B8lnUhf/8e6jwYoGqsN7vI1SX94bm4P7ms9FKB0Q5k+AWmVAFiCdAKgcaLkxGT7W9s/cSKTALT+GMZi7Sdd4K8ITRmNcj2Ui717lRH/m/rGwtVZfiZTBvnxP1dyIYP04/jJgN1b0fM09X3XXh/W1VfeX8jV/1NJk1KeZb3+tyvyb6v/7e31ckCp+O8a6n+gPRgpAPqLHZyAfiP+ASkZCdujws9YPOaRuylekKnXSADOaH3l380dSIcn/Jm/39T5VkcIkloLFM+usoEktgpGl60PnvW9yZk0kCgA0GGn8q8UsMEg8QUoUbjldjUeyyQBSiYB6HnlR7DdcsfvXDuma+2XoZL9N+WkZKEp6k3yek8u/u8kfVbjsRzumAGEtrhQakX4Go8F5fPXv1+q6vL+idwCH3eqp/7f1s5e/3kfEmOHcqM68ZrMAAAAAACgE4LOjWMchwTgI6wWx94xM34p033mr/q7PQHwZLUN3138k4F3u0qhtq8E0DzNP0L0m41/M7QcqHsD6Pm88m6/qO0ogLL4U2qor9J+K+lr8cq/v6r1SIAynMT/piS4bvCFJP3OlI5f1Hws6IN6r2zflUkCPJO5vv9JKc/ir+vn+JMQ/ZV/l3LviHtn+tJWSa/J+rrGY0E16ju334//nUm6kfTDwp/Bj2ytvtpSbx7/dun91l8FeP1xuu6h3J5nUzVrSgaKl17/9rrSZ38S/ztVFfX/em0eyrQB/NRe2y5Y36EwWSJ0c8WcdPxT/wPNtdxyG0D31TuBG0DdbNxT/6Pf0jv4bL/XsY+OLntQ9wG0XxD5IRhotloTeCDpeu97/DRSnP471kS3BX8mYynyvy/68ZsvWL3+uAMz9fo/iJKbsn7es/cHKNLTKDnA8jxTPH0oRW8k3WokU4Le5orHwCv/3IQNv4T2V4MJcz8f0G+2Ph3IDCt+limevilFX0i61VjmcV7niseRF/+hpNmqXElvfLq6B/EPHO1Zqj2drf7/hhS9UDnxL9lJWAO5zZBtWWDLgZfEP1ZGUuR3g61fP2bzQyn6Y0nnMglAX8hMgnkh6Y2yX5+vt2/Lrr+S19PJ5x5Imux5/o8jk3Azl3nVdV9vfztyCUB3kn5G/GMlSNUfeeP/Uyn61wNJ59Lyd2bS2z9K+o2kLyW9zhH/RRzfoc+XTg02betDyp/vRm4N5BtJP6o53r4XuTVZm3A8aJKi4+t/lqL3ZWqcO0n/pGT9v3+8YPvxVVH/B1K0KfbdMew7/m9FbtXtqaSfNKD+99dk/inx32nfTX3exZb39nrT9TXtaw8DqM5fRWYfHsnUuD8oND6rbo8DyOI7qeu9YtuffvwPJE2If6BET6PkWG694zfU/2gTVgDOySSVBonBsPXVdHaxa2+EO1YIzqcPG3lt57orZxtXYB3KJSwAyMcm1kib1hTZ55XSaxYVzSb82OQf/+fZjxeA70wuoSa7L2RXKbR7IuTjt31ckpKNfVYTAIrlr7CWfYb+KxUb/5IrAxaJn9jjtBOATgp7PmCbC7k1505kUhAuZBKCh8q3AX0VK/T5/Rzbtwzf5UzmFQ/j7z8v4KjyGMq8+xLtf5TtQpIemRsnZ9Ljt6Y8uFA9O34dI9+6wA9l3oCmGMoNCHMNgHLZ+t+mwN5JeqtieqHrWqE3295G9h1oiqGI/z65ULPqHwDVeSyzBn95+rBvBtBOF3ITAMpDGQBUwY7lSPTfAtmQAFyAYEO326FdSYGudSrXida9LbbagO3ZgSaYaCS7krqUvzxMRnY6+de/TCP+gWJljymz8udCgSaSim4P+Yl/JAAD5coeU9erVfrDQhIa1ic/+iv/nsiVA2wnhnVF1wp+WvtULin9RC5tvg3Sa+gfzu604Xfc1snGPfU/yreQXAFw54YsTtSMaOgfWx7Z20B55jJlwDS+7Q9ZtqX+t9FyXP3ftD43P/4BAN1Geh7QTyf77wIAR0gvBgo0GQnANXsktxbOVPlWANom24rEXWMHG6XNF34L737MIAHy8dcxb06Jk1z90yYASm6lYbsSO4DjzbfcPpRLLSqymzZcPaKfAJiM/bpWUAK6w8b8sQPrpzITAIqPRLMlq63/bbqVTf5diPY/0mapCWl53cRf7QbYbyV9JXMGtm1oYj26D4l3u87pmZqx4va9XDtgWu+hoHGK7tB/IUm/k4a/M3H/pcyq91O1Jy3B71HL7mbL7bo07XjQJEXH/6v4q639XslsRD5XF+r/Q9j6P1Qz1jz3j+G+tqNAVe7kaq+yzz/6k4FmuZGL/3Lae0Q90FTVtPcoA4Aq+HlbRB2QxYO6D6BsYymytxeSZg17zd+SIrsB1Z2kn+05vm9K0TC+PZX0ScNeT1YfSJHt+JxLet7y14O2+yByySFTSc9znY8fStGpXHf3y9T5/UEc/3YD6k97dv6P4vLZNt3KL5+fRm7r76Wkz3v1fmOfb0TJKTm/yHV+fDOObxv/v0id31dx+WDTUftX/11GbuhzKem65Nf/gff5LiR91rP3G7t9I0puKZQv/r8Rt9dNWqv001R8P4vrv/7G/7P4+sx25JQd/8+i5AQA4h++D7z6aK687f+ncfyfy23zbc+8uaSfpOL9G1JkhyfvJX3Wu/IAqNNl5G6Hkm5zxd8obt/bjUcfKnl18cNUfH87jn+bjp6+XgBQpiBKTg6f5Yy/IAo004VMGfAw/iqZ+v/H1P9AhYrt3wPQJt+KXA18J+lnxD/QG8X27wMA0Ea9WAF41/qvdTuXuRw59Ngu5LovumAo1xxr4ueDvjlTkRF2Hj+iXWMqzQ4MtG31j6LYss++P+WvwGm3/paYMYZ1Q7kOwvxsfG+L/0H8jAMl187ui0DXie/Lj/8TNbtFiHqdqYz4lzbH/53W16TvF39FhirqY7fCOFt+Y92JTKu9mHPRXq/bFX9vZM54uxV4mi0LVNgRAMjGHyDMZygXz2/if6G2rzd/Ktf70M/2AFCnwZbbxxlppoFMS/NOrrW7bX8M2x8AoAyvVGT9DqBNXsi1yPvY4w702SslF5wBAKB/epEAbDW5Q73Pg32k46CZijsj/bSTtL4m/0rmPam+O9Ze+PW51AXq5yc+VjMBwD6TxAAQqrJtAsCtAl3GSQJ9FOg28X358e9/EtT/2KS488Kv2+4lfSXpWiO5aQEvE/dfyg1PAmg3P/6nMmXATJdyqf6fJ+5PjQR0hx//C0kTBTJxb/cCeLnlLwEUrfoJ5wCaItDrxPfEP9Af1P8AAPQgAdjvUG/iIPu93Gb0d3vua+9vh7Cn5R1WZRZyK4AyHxP1m8ul6+YvMWy82kdOW8jF/TT3s7VTtem4tsSp7hnRJtMtt49zJ3eWbXo0uxX4ifqZjlrsekuHmMuVOLQ4kDb1bh/SIj/s0cIdj7ZQfycBVX9NZktcifjHOr8WnuZ+NHuG+QmAbk3g9bPfvz9nJ1A1v34o5tHs14WkmcZy+16tx7/fRpgWdhQADhOqjFZpKD/5d6ht9f+9d5v6HyiWH3X0/gL9Us+CEwCaIN3qJv4BAH30oO4DaJ5RZC4TLiQ9kfRYbkvQG0n/kPM9CyJJCjTTuaTrvZ9BEI00W20laFfLXMp0Fr6u+DMMpMj/fpZ6/kspsgnNoaTblp9j+14v6jaKkt9PWv35jKRoopFMtIeSbveWD65b45D77xOk3s9Zq99PdN0oSnbpdSH+A7nXtO/1XKbi9Zr4R490q/6/lCI/aWiyt73px38x9X/gdYvS3gXabBRJoWwfwrmSO258norvQIrSaUlmoDSIv9vdHrDXy/6uPv7j7e/v2Px4FuURkIWL/wuZXs2hTP/cQtKnG+LfX4HcLjZwTPxvSnEk/oEqHVf/295EKXv8b9rRz94m/gEAAAAAzTBO5RPkHU8DmqvzKwBnFWgSb8h7ItNV9jD+V+x8YdOhtn/+0Uiz1Zagi/if7bxv8gzmvq5ohjp0Z07/ZNXRfmiE2+TfoiOuieulA2nVryFbJhf/WfhDekDfdKf+91cAO/QagdY2gH0GMi0F25+wq7Q8tkSdKZCdQFDMHirueNpfugN1GGggE/encssZ7P6L4+PNT/71WybLzXc/6PGU43gAuPp/eMD97NesMbdp4o+/IMgxiH8AAAAAQPG6lU8A7EICcMqppNkqzdZX5HCWebyZRpIme4/H70BLrsgx2Pv3ZTo8QQHAoYI4pg+LbjvMduzw2jZ0twP1If6AvjHXBABQL9sCmWWclJROAk4/XlYk/wL5hamvh5oldiTJjvgFmiVLb2HW+t/H1EQAAAAAAIB6kQCcMpAUaBYnAU8l3ch1X9+U8Gy7ncms62cTfy1zjMd3zJUl3HIbKE93zrRRvP64LRn2JwDbkoFhNvSRPfft7T5i5V/0WZfiPmuiDS1uANuEiVtz7ye7Wg3ryYLHJQAWUSIlE5GZcAxkE656CRbeT++33FtKLjaQNfbTMb/c8vNjHw9Adrb+t7bV/35vYtb6f1tvTJ4YJv4BAAAAAMUjnwD98aDuA6je30bSk/j2K0l/06r3YCxFfvF02/rPcBy526GkScGvZ5R6/Fmp79dIivzvJ63/fPruw8htmruQ9FlJ52cYJ/Vzvux2FSUbaK9L+Dz8xy+6PEK7XEZmHRt7Trws+HwIUudbufVT+z1LvV/PC36/xqnHv+Xz6DUbn/acKPZ8CKTITeQbiPpmn6ep+Cy6PC77egRoE799IuWNB3t97JJ6ud4Bmivw+j/y14fEP9Amxdf//rAq8Y9q5e3f9fuf55KuOX+B1rD9+ZKZmpY1fsvufwJQnstU/27W/vzLyCzLJ1H/A2UrO9+jW0ap/EDyz7BLD1cAfizpvboP4mhn6uL8hKpeyfHbGB7zLN35fPrsTuV+opwl2ZzKfR6LXXc80mDLbfSTn/xbVqxSBhxuIGkY356W9PibbqOfTuU6/LJsnHsMyoH9Bkp2wJb1HACSkx+KK5+IMKANTr3bxUUt8Q+0QfH1P/3jqE+6Ptu/x16SHw9nu+4IoJFs/B7Tn1dF/xOAZvIzcE523RFAbsstt7GJf3UC7NPDBGBp0waXbdG9AG/vZ4E+sBf5yQ3xihLEW9rOFGimkbJ3SPaNv0VDmewmiOi3pfe1vPi32OD6EGXGZVXlC9phIFcGFD/hxK7+69oBANAU5dSFtK6BtiABEOin8ibEcpWN6uVdQsf2D9GCBdqJuAX6aSGuQIG2KCfvBkAvE4BfyBUob2o8juP4TZduzD8su3CvPqGH6qpLFgo0W3X53Rb86K4b8VTShUgA3ieUS8Qqa0VmOndhLeJ/oUaalRqdJhmQFMDdFltul4EyAK6uKSv+bfLvqYj+/UxZbLAjA1Cd/LHhX42TAAT0iy1BureQAYB9mF6Lep0q3w5SS3EGA20Vbrl9KL/Pmb4ioF1CBV4vfvb+9rnMJKJQrEgKlI06Nou8rRv0y4O6D6DvrqRoKmmiDyX9G0n/Vjr9A/PLxf9P0n+U9B91pee6kPRZ4Z9ZECW/n3FOAIXpVnw9kyLb/bmQ9LLyOmQUJadBTDI+/6X3eYSSblv9eaDpuhX/H0qR3URxoTLaI/uMU/GfNX4vI7d101LSdas/DzRbIEV2lV+j3fH/TIr8DRCf1xL/vmPj367oTv3fLnnr0+9GZqKdJH0l6cc5P/9x5FYVCyW9btn5NIrfz03dhcntvwNNNCs43sfS6vMMJU3ok8JOLv4DzY44H/82kp7InG2vJP37nOfbU689ulB7419aLwM2pekWW1+O4/4E+8y3xD92yhv/fx1JD+PbbyT9oID4l1xCQNuuJ3fFv7ReBhQf//6zU/8DAAAAADYZedePUveuH4PU6yu6/x391sMVgJvFda2dS3pP+pd/IP2fZfoSf/6H0uJXkgasmAG0WjeidyCT7lCXILUG43ErJp7svwtQmO5sOeSvn1KHILUGe/b4P1FXymI0XzL5t/3OZK5U6pI//i3aAP30SC4B8HTPfQ9x5j1Om+t3l4ZnEquqK7eojXGM43bMeCLpvQKPou4r4iKld77x1+uVyijf/NXA21x6onrHxf+FTBtAKu6M60rv/LYorGbd3i68gwAAAACA8nRndB2oFgnADWAKrv0D0hRwAPqMQQK0y2DLbRwjuSYh0AacsUWhBO07/ww4Jp4G3r8iEve6fEYmkwHLeKWUiMgmb/z7f9Pl2C1SeVFaTWohYNmdH4rin8FtPJP943fvi5sEZH9fXhnA1REAAAAAAEB5SACumev0mkr6Uvrdf5P+yx+YXyz+SdJbSQuFMlvuAmijbgx1+eXQos4DOdqmFY6AKrT/fFtsuV2H40rT5ZbbQBm6Nbw933K7asenJFD/d8cxNcCdXOLvXUHHEMptAd429V6X2HeOFUCR3THn7Svvb98WcAy2FdzWM3g9/oN4VdVkAqC9bzlHABwm7wQAP+aLqP+n8dcidhOoS/J9DNZWVS63jUD8AwAAAAAOxTUkkN2Dug8AAKoSSJH//WxvGfg0MpveP5TZOvAdmc7+haQ3kn5IGZrLOHIJGUtJ17yfKNFl5FbbP+R8+zBy24Y/kbl9JjN4+ELSv+N8zeUqSiYYvOb9RIk+jEz9PZCpwz/dc759O3JxfyET+0u5+P8B52sul157LJR0y/uJHUZRMtlukvN8+UYk3Ui601gT3a5dD3w/kh5LOpcpL/4q9ftnkfn5PD6mvMfTd0GU/H7W6vfzSooSrRv63HIapdqLeePtm3H832ikiSZrn89fReaaX9p8vT+K2xNnov2an+2fMcnHEvGPpFGqfsgb/x9Fpi1/tyX+vxOZdn8okzz8s8TvR1I00ViuD4v4z2MkRf5g7v7+2WYbpfqb188vNNtHkUvuv5P0ecM/v2ztZ85PYJfLyLXt2zA+lT3+E1dTxD/gKfp6o2zU/wCA/VgBGEBvzHQpkwBotwK83fMXA5nB/wuZRIBHMgnBU7Vzxa+m8bdP5P1E2c7ktgA/ZA3bc5nk/yeS3o+/nsoMBnC+5mc/C6AKU5m4tW2AfS5k6vz3ZMqBodzkn/syDrBnTtS1VZJRhaJW3JyuHm9za+BC0rvx1033sHtiLOJts5GP+VzdKqSAb7Dl9rHsKqCnmijU+hn3jkz8bxYnDYq6q1hdiX+ubspQZHtxX/xfyJQBS22q/4eSJolVwJFH10rR9LrQk1qOAscbyvQBhqp/z6tycPUNbGP7p7pZtxd9NQWgXaj/AaB/SAAG0CMnMt2ydhVfNAOXH6hKlq6uk/13AdASdqXOQ8sAv/OfsgCoX5FtRZvEP4hXndyVcrapzAhlE1YZQEPacsttHKvo7ejvZCYF+hNRfLs/tYGkQBPNNCrwmPprpmCV/AuUbyq3em/2cmUgaaSZJtT+hXArf7uftNlQ7uyaS7qu71BwFFptACRSZIE+I/4BAN1AAjCAnsmSQBDKJAncyaz6F8ptB3xT+JH1k+1kJQkYZfNX8jjkfJvKxf5AbsDwTtKXhR9d/4RbbgNlWMidZ4d06N3JbP0rmdi3KwffyJQJyIcUOdRprt2rCd/I1feb7rPc8nMcp1vJ1KwRXwabBFxE3O2bBHwjN/Hnq433GKyOCUiaK5kAiCIUGWsL7b4OsH18odxqwZuOhPgvhj/hsv3v6YVMErDk9ppAmyzk4r6bJXj7owwoi+2TKup6o1n86ZTde3VAXt2PkG6+KgDALg/qPgCgXOMo+f0t5zxQkpG0ird4Q8WWxVuwOv54S+eWHT9QHz/+JWnSuvgJUu2FWcuOH6hPN+LfH+wh/qs1SpW/k06//yMp8tOPblsXL0CRRqnyt9vxP/biPxTxj77rV/0/TrWXiX8A/fU0MrsznsgkXD9vdXn4D1L0JL79StL3KN+BHT6IzJQ5u9jK562Ol0+k6P349heSvkn8AzvY+l8yPSLtrv9/mKr/v0/890qQur4nn6Rpyh3v/5YUXcS37yT9rGGfPysAowe6PYMLQPH2bwkNIK39tW37XwGAYxH39epP+dut9e6AvAapr902kFtfGIDUtxqxHyUdAOxzKrdmdvs9kfT+3nsBMM7Upfh/X9Lv/6v49j/XeihAC5yqS3vmvCdX/3Od10987v11Ielh3QexAwnAAIDC0fABAKBfurlhIgCgXP1KAAQASjsA/dat6VDhltsANvFHDYkYoF9O1LXRg261aJAFOTD95i/w0sRzgQRgdFyoLjUmgCYL1cyKDkA12l/btv8VAHUignC8/p09oaRF3QcBNEJ/4t9/pcQ/0D9h6isA9NPS+9r+FtGrLbcBbBLKrPxpv7bbF5K+9s/uNoBdulf/D7zb6Bc/H4br+/6Zyn3+0/oOY6sGJgBfRcmQef2gzqOp3tPU63+Z8fUHqb+fHfH3vqx/3zSTlh8/yhRIifN9JtV8vjyLkltgPM8R/1LV53/V7983pGgY355K+kXq+QMpOpWbU3i79/jaXt4hi+bF/wep+P884/GMUvV3tfE/qfj9+1iKLuLbN5J+mnr+UVw+2Ph/TfzD07z4fxqZbbAkc8bmqf+Paf/nU3X8f0uKzuPbd5I+2RD/pzKz8JeSrol/NMh6e/Qycqtg2IGwhQLNJDWhfMrLb58cUz6Zvw800bkOiWe0S7+6zNfjfxS5dSPC1b+uxH8gRYnWSebX07X+SfTZevyPU+33bsX/MylK9G5mfD2BFM0UxN8NRP8+uuXS6//r3vjnt7z+uqnW++uy93c2268kvY1vf1XngaAlup3/8B0v/u8k/Xgt/j/t1Ov9R0kv4ts3dR4IWsL2/0lxj3Wn4uGb3njcvaSfdbz+/7Vc/U/890/br9e7r9z+wxu5xN8mTmdoYAKwv2gy8uF9BNrlTC4B6Bj9ivmhpIsdv7e1yYnYigNtcCZzVuMQF5Iexrc3pYsMRIsSbXIqF//TI/5+sOV2N53L1f/b4t+mU6INup/0t9uJXPv/RP770Z1z2CY35ntFtOe7qs9lQLdbq/1qneA4fY5/X7e2wpXy924C3eZfrXYr9iVzrb6rv75rfi1X3jUxAQBNY5erkbp4xlxIelz3QVTovyi5nAuwW7d7q4dy43V98Bt1uTUHYJcbNTv+G5gA3HfhltvVsCsNWLMt9wNQhqVcl9Fy1x1xIHtJ1cQKGAAAh5qqKLyTaB971i43/rQb8rTIww4PkaDf0v1/4VqfHICu61ZtX6RAM7lVgIGu6Xbs96kv/rVGSqYATGo8GjRf/omxaI7nGqd+clvLcaBtullL9q1ke65R6ifU/0Bf3NV9AHs0MAG43gTY+i233D5G9vfPX3+kj+8+UK9QbuW/YyKwXx0I0y23fcvUV6C55ltuY5O7Lbctu4mqRHsGbRDKxf0xK4D0awv1e+/2dMt9qP/RHnMlO/+7GMN5XpP7W+K5i7p4vmfR7aTfvK0TFijour7H/3qbv0tlQVG9GyQBo5ts+/9EXWzh7uuv656hSADG4UKZNkA3EwCJfxKAsUv36nxf/+L/VG6vsqWo/4H+MH0Ufv3frL6cB3UfQPuMomSC3S3vIYCNxlJku/Mk0wRMD+2basHOFKc8AboiHf+Su8S3XX2ukXgq6Zr4BzpiFMd/ekpO6P0z8W9X/af+B5oikCLJxW96guxtx/tQ7Ou3ZqW/3iBKfj/r9PuLZgvi+ltKDuVIph1/TfwX/YzEPxrDj/9NbfgJ8V/0M0Z+wnP5zwdUydVvgWaJMiUeHuZ8BzrLxL+Nfb8vgfgHui4Z/76JAnG9C3RJ0/qzmnY8qFMDVwBuOn9NOQDY7WTDz/xSxK1kQbkCdIlN/k2vepVM/uvPip1An9hO/m0TAFz8E/tAEw20OY4BdFs69u1tamug+9ITf/yJ/ChHcmXfZq2YAxStP/v1AdiEMgDoJxv7I81YHxYAUAkSgDO68mbuhJJe13kwABrP39Q3mfgnaW0NAABdYyN7LrtykJ/4a61v/wmgvTat/p9c9dtH/Q80iZ8A6PN38gDQfQzSA/2zaQIAkwAAHGvTCoAA+ovyAOiPUMmYJ/4BAFUhATijdyVdyHQGzkUCMIDt1lf6tVt9pxN/QwWasd4F0CE2+W8um/g3UnIzYZcWSPwD3eJH+b3S8W9jn11FgCYLU19dEj81dlloD6FJ0mXAvK4D6QniH3XzW+VnSl65E//lIv7Rdf5iQvQAAP1h453EP6BfZgoUaJao8ykHgG4x17DB/jsCNXhQ9wE0XxCNNNOFpCcyCcCPZSrrhaSvJL2VdCPpTtJU/up+p5Ku97zHV5G7PZd0y2cCNMYokkKN4zLgocxAgGTi/Y2ka40kDeOfmjU+g3gzj9laGfs0Sq4h8px4BxoriCSzPc+pXOxPJd1qLNMauJCp66cypcKNRro1tf9a/D+LXFkRSvqM+AcaKpAiKbkKqEn+G0urFsEw/s1U9kpge/w/jVwawVzU/wCcDyPpPL69kPQp5UPCZeRSsCTpdcvenyBKfj9r2fGjXN/y4v9e0s84PxKuIjeJeiHiH93yUZS8nqD+T/Lr/6X2j680DfFfr3zv/8dSdBHfvpP0E8ZQcxp7n0coaZLx/TSfZxBPFVgfbwF8QZRMu88W/9+Toofx7RtJ/8D5ltNlHP92CsRx8e9Qn2KXfOfLd736/ytJPyb+cxp78S9ljf9AipIJlsQ/iuTyfx7KjPgN499MZdoAN5ImGsU/zVp/AfVhBeC9zMXCqUzwP5ZJBLbpPpLphp5q01ahh8zptZ3Z6Q0BADTFQKbiH8qUA/Zy9SxxD/s1TPwkyU8jBNB0drs+uwKQ5NfstmVgo/1ektsydF36EgJA+6RbBNa9pLs98X8qU4IsyzxAAK1zLlee3Nd5IA11omQCINAlF3Lxv7kF0W+nctdap7vuCLSQ3z+AdX79D1RrqOTVPvLKO+7pxluAbLKfdw9lxv9RFGIX7fEw/oeikPeE5rPj/+mr83v5Zy/nMdqFBOC9djdQ0yEfercCTQ7YwsomA9rtgAE0lZ/kv17d27SfxWr978nafdj4B2gjexEg2dSTTRv47Itrf/VvAG1jInz3pp3b03u3pwYDgHEirhEAAACkZJuI9hGqZa/6OfOKsrsfBSge51tzMIkX6K/89X+gmZKrAAPlsaP3doxvsfopVwVoFxKADxTKLfktmeS+Rfz9VG4VYLsU+EgTPZL0+d5HZjUwoMns9lK20l8o3WxNN2JNQ2DzOr+2BBkWe5AASpNu2g+keILPndzawAtJc0m7anR/VT86v4CmG3j/TmTierJq8furUU1l4t+0BTZ3a4XxfRYi/gEkLeTWFZjWeyiNZctN+kzQNXdbbsPwW1W0n9A10/hrKOr/TULvK/U/sgm8JXmOSRrxa+Rp/sPpvbH3eYTatGDKYWYKEp8tsEn6HMl6xtxsuY3jjFOfwG1Nx4F+KCr+Q0lfFXFAPTeKd1eVjq//Sf5FFeYyPfP2CnQaf2/OP5J/0T4P6j6Aon0oRf4GkZ8V/Bq/JUV2w+9Q0o/2PP7fS9F78e1Xkv6q4vd8LEX+97cd/MyB8gRRoJnO5bb/som9U0mf7omnj+PyIpS5eHgh6bXG3iN91ul4HKXKn0nPyp8g9fpnPXv97ZeM/zOZun8uMxhwvefzvJIiO0vQTA4aym1ju5D0stPnw0iKUhf4nX69acR/uwXx+Wv/+XX/rQJJsz2fZxCZeLclhx8Nc0nXnT4fiH/iHzheECV3V7glfoDeGMX1p207dbu9BMA38trP1P9I4vqq3a68/oG59venAj7iv92eevG/kPSazw8ZEP/tZut/O52P+h/NYsb/7Qge9VNe5v2UzNV838YDm6ZzKwCfqty1NV/Ipe8c4j1J75d3OAfxB+ABZLeQWf3vTm7dmfkBf3ch6aH3/VTSVLearBIAu4/yB21nV/625/KJtq3wve5EdkPviRaaaKaR+jJjkI1R0Gb+yjJ+GtpQ0kAzXe99BP9qwSb9hlrfNaCb2LAXwPH8Urf75SWANHoQgP4i/oEuGii5hxKA/rCJVdTsQP/Y+j8U4wNoHjv2Z3f9Rn7s1tEcnUsAtsPqZVUmr+Pl5tt0AlNwAccaxAl7oaTZavPu4x7JTVAIdc3WFUDjmfifKVxtV2VT+g5pYyxlkn8l09F1Julck1U75dgt7wBUwdX/A81W1xb+asD7/t6UAGYd8CCe+mPT2Yh/ANiHXgyg3ygDgH4i9gEA6BoWCgH6i+n9aDKXq9OevD9gn84lAE+924es0Jmdma86W81bvd157zdylds/lXI8u1GpAnm4S9NJHMkjzQ6+WL3zbk/lVhE9l3R+0AqC7UcZhPY6lU3gu9VAozhl79D4t+f+iZJr2UmmfdL1BMBwy22gHWw7P9StpFFqNeDDLCWFCjRZrXghlXV90iz+ZEziH0A2lCBAv4WprwD6g/jHZiSOtJu/DxKfI9Aviy23AXSf3VnV3gaaiykqebVp4dQ+eFD3AWT1Qyn6Y5lh+beSfiPpV5K+lBtQ99fcui79NV5GyfSelzuf76kU3cgc/xNJ78dfJemVpB8WfrxBlPx+1rrPHH32YSRN9VQv9ReS/idJ77wraSDN/1n635Ue31dRcgD6da7neyZFNgn4VNLnNZfBIylRPkz2HE+Quv+shXVIlXi/8vogku70ka71P0n6y1NJfxb/6ufSg0XZ7+czL/7vVUT820GLUNLLms+HcXw8io/nlvgvVJB6f3m/snoaSVN9pFv9haS/lHT2vvnN//qFNC79/XwauWHGuaTrXM93JUV2y6u5qrg+2e1SiuwK5YdcLxH/2RD/xfqpFL0X3/5C0scNqD8l13m9r/3cN9nLiyDyOwn9XVICzTLHj//8m3dcoT+kTb4rRQ/j23eS/qHmeKM+3Cdb/+OueC0u/v2BnAmfV4s8k6JhfHuu+vvPpHF8fq1aAJxPCcfF/6a6utj6317TUf+jO9p+vTmSInPcqxXXWnX8QJ2yjuc1DfEPHI/6HyjTODKLAE70UNJDmR29JbPA3ycFx9soFc/J2JDaFh/7+ksDKcry+uh/zad1KwC/J+lrkh6cSsOFGQR4JZMMfKtA66vFVJVxftj8lZcaSZIu4gLksUwC8FJlrQjGrAW02amkpQYycfLOI0l/bn589o+Sfl3289tUXamIOap33iMOcz9afpQOaLYzSfc6lZksoz+T9Bfer//fZT//QG7NzvzsenZNWbnETpYCmulU0lSnMm3/sz+R9H+RtJR+fynpn8t+fj/+B1LONfv9+rYJ8Q+0yR9Jevwvze3wd3UeiUMcFye9QkA5KwZw1dNWDyU9qvsgcLB0/GaJZlYLQdpQbk+eZmnKFX2zHBv/5cZ+ei8moBsGW263hS1BbfzTAgD6gxYUcLwu1P+bJ+oDTWDScG3+zlDSRYnP5ucKJHsY2hjd+xH71WpdArDNgj9J/cwkzw7k0uqWWs2BKf2IsriQmSvgLOOvw/wHswVbaKLNwvXqzt87qkVuFWisWWMO308AXO664wamsqaLDhVKV/yVsKVP/mFHu+VNN5vv2KQJ5XynDJS9smqIpm13uVDzjqlreF+7K9xyG05zex+ad0RAl+S9zmHCFNKalfzLWblL1vifKSDxH+gp4h/oL5sAxAQAoH9IAETTBZrpRC451yqzJ6Ar04vX45savk6tSwB+pXgFgIX0RtILSTeya3Oebv278hwXlku51YuXMsm/5QW4nT8AtM1U0kKhTLx/9VZ65z+Z3/yvX1bx/DZlz97Oa7CasNCEiLQNmaxoqKMac0lLLWTq+j/6L3Jze35exfP78Z8/67Apce9raS4lesHUuQuZtvLXfy2dxLNnvip99V/7/MVFyFKu+GrCBf1EIyVT9CY7788AXTZ0eBTrnyQtf2fO1N/WfCxSM2K4yfwemYGacvbzqbXVnXf7TV0HgYP5gyTHRF25PZeUA23j75I3resgcDB/rd3mRFtzjgQokl9ftvUsp48BAIBs2lrnA+3gFiNcSLqXG8ublvaMya+bvmuP3VcngWaZcosYX8vnQd0H0HSBFPkd0JPc79llJC0UaKILSeeS3pHZ2vChTCLwQC45+AtJzzWSW2j8ZcM+s1GU/H7SsOMD8hhHyUrrNuf5PYoCTVYDY7eUwUCDPU3Ff776t/j2BIDyXKXi/3WueB1JkX0kSZoR/wC2KL+98HHk+hbuJP2k0Me/lCJ/h5HrlpV3I+/9j6dHZDz+cap/JO/1I/okiNsLVvHthe9HpudRMksp/LDQx79Kxc/rlsX/2Hv/j4v/y1T8X7fq9aNe5cf/x17830n6MfW/p5j6v8j+434bpeJh/+dxFUlzjXWrJ5Lek2vt3kj60drf5+2f98t7Pu/uC1L1+4zPu1RZ3+9nkTTVpa71R5Lel/RIJjLfSvq7LfFvJ67mi3+J9l7XBVFyiiL5D+UaRcnpZPve72T9/0Su/v9K0o931P/SMe29IDLT3u0xUv93G/U/cKz819fEXx6tWwG4avZCoLgZ7PeSpJlGmsWP+lYTPYl/eyHpcfw1lOmkcPN6m7Z2YFpbZyUA29jVP4sqAcJ41op3iQGgoeyaQycqYiVQZqwBbWLX7C3GhPgHkFF5K+jZHodyHLvDSFPkP/aqNolDV5W7ot9DadX7WLw29Fruk//4z1T0ThJAMS5kliApR5fq/+Nex0DmXUAR/DrQ9KXtu342NdBAZnGdC+2r7Qbe/8egjdcvtO/b4lwm+feQ+D9eF9bgxuHa3Lppo3DL7W3c53MqU/8/KviINj8fsd8flAHAMbrQP9hmJAAfoMgT1C1xfSq73u9EF3qll3ook264jJ/TziNqNho66K70VlTFpOvYau9MJAADTbaQS78poq7LuykugOoUHaMMGAHIhpKifsUmAwMA0BS0MsqxFEnAxXAT6A9tS80lhRunX2x+hLwxsNh/FwAVma5u+T345SWeEP9AWbKPx0+Pfq7jyoeixgnRPnzuQBb0iNeLBOBa2Fnh9muoiQLdaKY3ckFxJ3M5YZKGF2piuIxSTTDSGdElxafrDMS8F6AtwtRXAP1SxvqblCcAdpspUBDvFVSOuy23i1Hs+unV8weMjyuxWREK+ZR71tx4t98W/uhzmWnO9nbbFPPet/GVox/Kr/833W4Lv/bO9whLUf8XYeB93f9+BrrWTCOFMntv3niPcLP9z45O47lMjYddH/EYaBNiuko2ATC5i+Z2I11ropEWMrXbW7lR983xH3r/Z3epWaK9S/x3Hdf3VTqX24vzkFT7kW41UaBQJv6/in8+0P7W7jGf5jjuq7PHSPz3AXEPHMOWk0ybqMeDug8gvyAaeQNkt514Tc4zKbqRNNFYptlyXerrC6QouU35bOfzXUqRndseN3g69f6jakEkaTXoPdlzPo2l6EJmkGkp6XPOv1IFUmRvH/L5ANlki/+PpOiJzGr5d5J+wvlYKj/+JWnG+41CZYv/78TxP5Dp0P8h52PJgshfhYH4R52+J0WPZDqS3uiY+Dfns+0/WC9vnkZugGch6TXne4+NUu0frn/q9bEUPY5v30n6UcbPI5AiP41g/fN8lor/l3zePTaO498OVhD/9fpAit6Ruf6Xsl//X0rRQu7zXG/P+vV/KOK/3/z6PxTXP03zvbg+X8gk+3yS+/MZR24Ky1L7xt+eSZGfAPg88/Nfeu3LUNIt59cOVbfHbXtxe32BOn1Pih7KfD5vJP049+cTRG7BnlDSZOfjfShFti2ykPRZ5ue/SrU36G/YpY74t7dNnsTu/AhU61tx/EtmPOBnOc+HrPkwxdT//m4RxP8uIynyEwfLr4/d+AfxX71vx/k+NqE/b3xnZct/NwFpd3ugaWx92dV8SWTT+hWAn2q26vwLJd3WeTAlmMZfA90q2RAph/8cZuXh3ew6xkAdnkgaypyD01qPBECRDpkR9kTSo7IPBEDlDo3/x/FtNt8rXzL5N9Ahm7ABZXkkUwZIx62zmN5ScLNB6iuAJnis4tr/m6N7IJcABBhl7AmB7C7if+dH/r29Zthes/vxz0rOoBXYZBcy4wHFReqZsnziQ7nJCMedJ/6IGuVN02RbfxpVeyjXH1iMbLt1nsuUAdKx45HsDtoWh+RHoFrnMm2AoiIoa87Nmdy1SP76nxrmEFW+W4f1F6MsD+N/dUeGKfvLz8crA60LWL9X9wHk9cT793DPfdvoPv46kDSqsPKhokPdDqnkbYNgKHfhDaAfTsXlMtB3bLAK9I9dKyPPFlKHdYhRugBNw3A5qkQt0Cx2uPxk3x238NsN2ycAUMIAbbGs+flPdHx5tP5I2CXccrsK1ArN458DxXw+2R+FFgNQj+Lj3zkkJ8Y+ZzE1d90tmXapuswlR6p6Z96/uuvYtn/+9GWh9SsAP5SZ8SOZJf+7ZqZA/halVchSsC233AaOkXVmzcD7x3z58tnPJtCMBgRKkaX+mXq37wo/EgBVyxL/NzKreC3VzfY/gO2+kon9hcyWn8cItaszcS7XVcYVbt9xzdMsd1tuH2PzZxvK9SywxwAoA5pkLhedx3wutn99+9/7Mc8nD86CJruTW511WsgjZhvhmm+5fdzzYZ/1saJ2J2Ugnxu5a/lixgNs78BhU4wXcot2HXe1UGdKO9Bud3LJgXXkA/g9Bfnq/6VcSQJActf7fq8csrEtGXbwxIO6DyCvv5YiuwXgW0l/t+c1jaRIck3rWeXvQRAlv58V/PxlP/4ofvzQboFR8zk0Sr3eSanH84EU+RvCfV7760eZbHlhTfi8G8aP/1D5y7tx/Hi20+eWz7vHxqn4vyX+G6bo+n8cJdeUJv77LJAi/2yov72LpMvIrbewlHSd8/MZRcl0UOK/3S4j0x2/uuJv2OdZ9vV61z2N3KbLC0kvM71/o1T57jpG7U/L7U9A2car/qpirg+LFqTam007vqZ7FpkhX8n0yD3P9P7513fEf/cEUuQ+z4Ga157z6/+BON+yukrV/6+Piv9k7J9692ja+YJ8nkbJhL5s5wvyGcft7RPFV+uN60+x/Qn2HMl6fhTdH4FifeDVt9mvF5HXKHW907T2Tr72hPl7//VRvzQL9X+9vh25JRunkn7SsPf/yjs/jqm/qf/L9B0pupBb8OOFpGuNZfb/7l48fxy/XslMbvjJ3vayrV83568k+0NO5aZLVFUe/iCS3ouf85Wk30r6lQI917nKuB6oNj+wbVq/AvBA7hQ+ZMafPdXt16qNUitntjf/ftCgGQTVbQJ/JlPVoD+qO7uQT1HrpA9SX9FnxH/TFfkJ5dlQFl1zuv8uqJUdrCuKv4EjJX772XV4mt6Wq6tHou1sJ+Zxtm/7jm5p6mc62HIbhzlT3laa/65TAnfLTKO6D+EAtDePl6/+l9Lvvt/3x+fRPf66YXy+VbPjtc11onztibx/j3LdKZmghWo1/Xonb3vCJlZJ7BjTRNT/9bqQ2bddamb8+3tKH6Po8Qj47uSW87C3831ezeZHy+G2569sTv61/6qor96T9DXv+N5KOmPUvSatTwCeylTph4a/X+XXUWSU3dmc3j65+PRcf8PUbha6APqMC0Ogn9iCDUm0cvuE+O8W/3q1qbiePp6/NWtR8dqGcwaHsedEGz5P6hugPE2OryYfW7dtf+fbUGcgm/vEGFkTlq/pEwb5UadA15JsIgyAPiH+m4B2NY5zJ+leZurOvaSJAplWJdfPzq7+63SytL8fRxU2pfoyEasurU8AvpGbVTo94P5dLyaqqVqbNkBW3ac633IbQBd0vYZAFpwNbVBke8RPKELfkQ7adEsV24HAJ94lgWapLd2bqGnX020Syl2JZ1/BYHu0E/vdUOd+X4do+vE13VSu/j8+/tfffcrkbgnjtkATmbOvucfXZH7MZy9Dt/8F5XEXXWqWWP+TeKvWcsvt5lgq34rszX+FfXYh++manXhf13o0feS3q5tYx+ZrT5i/WXi30STnMulnF5ppKcXpwKjOVC7+D9mzvWpz5Vshnvq/TPfebZd/1d33+W7L7e2y1DmhkuPdVdRXr+RW2P9S5lWFClXWp0g/3i4P6j4A6TJKZqBf5zqmb0rRQqaamco0xSaJAcBJA15zfwRSlJxtNeP9R2cFUuRXN5NGlLEAqnApRf7l4zXxD/QG8Q/kMYrMV5sUl7xeH0tRKHtNfyHTpW87lOaSPu9YvI2i5IDZvv6LZ5F770JJL3fe/wMpGsr9xS/WyqsPI7NdmE20bdr7O46SCZS3qeMLouT3yf6HQEr8frb2+nf/PYrmn+9S+vMcxZ/XRCNJQ3U//sep+E+f32kfRG4Aay7p+c77fyMV/z9dO/+/EZn3OZTpUf20Ye/vvvcnb/yPvN+Ha3+Pou2u/+3nNdNIyW0spUPqu/YZp8rDfeMjT737LyS93nn/D6XIJkXMtan+/8h7vKmaV77ujv994w9+/Jv7peOb+G+2cap8310/PpMiv7XwnOtz7DSOkgkSxH+z+O27gfZdH38oRafx7amkz4l/7GTj35YBxH+zBF77b6BD6/+lzCdK/Y/dbPuf+Afy+14kPYpv30j64Ybr9bHcSsHp5F1/bGOu/f2h2KUBKwDbD3mgItZUXcSPNIy/dznt9jkmuZ8DADY5VbPntwIoz6YmK4B+IP6BvAapr06yTb2UW22lq9uApbfsOuT+Z0qujLvdUG5VpM3rZ9oEy6a+t4tcWzqvbwW57RFYpbUa/vm+/n67T6G8NSOaZdOWebucySXs7zeU9FC74v9CLgG4eQLdJr4vL/4lWnZV2V7/u5/4SRn0uDk2Kfqw9+JcJsK3X7fYx2umQLc7t3P2f7dpheVsW0ET/810ePzb2hE4jH9OEf9NE6RK9H3tv1O53ABgv3QbE02SNf5t/X+qY/aLAQAc7zeSXsS318cnzMRuy+/ftZlVdsK3XZAg2f+HbBqQAOxvuZLfVK6Bn3XoDMXL1sEGtNtQyfU3APQHbQ6gv9gACiifSebwa9o+tLYPaVlkS4LyH3HzX2ZJPqxekDoLykHyb7V2b9s2kI3/9DAen1HWAevd6daHP05d9h//IY9gcf40nS2JXf3fpy2XD3l9fmL04bZfq9jU4OMet2wmmcOkfZidIbaLV/hN/ZT4b7eqt7AF0BTp/vasE8CA3dhCvMn8Bbek/fG/FBOAkAVtSqA4Xx5wH9enY/p5Apmpyu/ILUggmWzP54UfYZ80IAFYKmLlX8uuAGxvJ4tvCvPqucGzTTPwgS7xZxhP6zsMADWjtQH0S7jlNoBD7I6a5FCMvxXUibqZcp91BRqbDHVY0upC7p2733iPpdxKy9MDj6E5kqsD75uMvOk9ZvCvWnbAdXc6qvnq9/bZv+2arK9p6t3e3686lXsH77Y+/1Tmvd58jyY7PP63vc9+AkAXz68mOqwNEGgSf7o2Dairn0+W15Wt/rd1/om21e5zuQ2TixunKYq/PvpSefZ3PCT+0TSj1Ce+7/Ofb7kNbEb8N5m/Pv0hteRiy20A7eOv6H/o9LiFXIsWAFCNcSoDML1+b6CJBnLtOrPEw0w3mmm2Sv59N/7tVNJPSz7ibntQ9wFkdSlFkkvunVX8GkZSZJ43kDk9bxPP/x0peie+fSfpVfzvViOZC8nbncc7kiK/u3lS8OsL4vfPKv79u4qSHeavW3eOAXV5mor/ly0so+u0v3wLIrtq2EDS7drvn0YuhXsu6TnvPyrzTIrsJhdzSZ8T/5nkj/8PIreO+72kz3j/UZmnUmQ7NBei/s/qkPg39zNlwPr11bNU/f8573+DjL3Pd6Hqr/+LN4rsUMRIs8zX+0/j98Om56zXZ5uez3S0nW68/yiSHsrFwKctf3/RJZep+C+6f6x6QZT8fpbp9TyL+wtCmf7GLPF/Lul6Y/w/kmsDf9Ly9xddMk71j+0/35suX/wfW/9Locaard0/kKKZLuWG32j/ormuvPpPkl63vjxIG6XKh0mpr+8yHt+0/8q/vjL9cXbhlAuZq4+Hcut92b7QtzJjqW/lJivYTYEV3+f52vGOV+WdUe77h2rZ/nLJJPV1r788X/sgq6u4/3GgqvofTfyfy8T/Q0mPJT2RSfO5kInvG5mNw38l6QtJr+PJfGPNEruursf/pff+hdqXf4F26X78V1v/X3njD1XkHwRx+83W4379/zj+XpK+kvRLSZ8okPQnMq2DpUzJYP5t7j8l/nG88vPl2qba8mjd9yPp6zIlw1xmReEXkt7IlAMve/75ZNOQFYAP58/aqWNepnv+zZt92wpMctsNDBJ/s9tgy+328OdkMscayCLdoYViBXvXIB9qfWMZoBpnMptd4Hi71uvy439zhPtbjADV8lvPON6u2tufALBuKOK/2eyAf3daaPlWL9x+Lm+3eTtsySX/2iFAoFnsitXdin/pmDLAroBpV8FMr6ixzUyBBhvj30/+Jf7RXN2JfynPqzlVlpLDtZw2RbdL/t12D6A5/J5aztb86rqusqt+DWV64N6RSQB8J/653XtgGt+eyx3r7uufru8Q0W/+CqDdVG00Zl1RuQj2GuZMLgn4XUnvySQCPxhID0NzzfdK6++IHTPdvP+Tvz8AusYv+7v5CVe7A01d+Qe2/j+Vqf8fy8S/zaU6lZn4Eyf5yrX87HJJgx3vzsnW3wDIqu7dlmwpMZTbocz+Qxa/V/cBZHWroCEX+naObNbf7H/Uw+/dRDaF6VykMgCo0iHdJbsvp45JpwCKQXd1Pvsi13Y0bL+EOTngUQA03ab4HsWrDR02YIgm6la96Iaw876uw85c9yyzeBWdzY+yEFMQ0TSh3EBfN0rqQepfNscviDDQZGP8+8P/xD+axe9b78awbr74l5KJcFlsrv/9d5X4R/PZM7Yb7YH62fK1Ke/npuui9M+6dU2IQy233EY3bIrrppRLqF+Y+od8BjLl6FLVvJ8zBYnPblNs+1dHZrLHQuvTvSgVgO6byuzMO5Xrn7BTB7rRI1Sl1q0ALF3EQ7iLA1ZzLF6y02w9FfnG++mdzKlqKrdsc/Tby27mIbX9lQBV84fdmjHRob02rXLmzxbfPkljIQZAUQd7xnH2HWdfi8O/TNj+/t7LdIHcF3NQwIH8Op/W8/G2vXdDua7C7fFv1xeaFnlIwA6DHavybjfXMSv27Opu98/76YbfA/WyCW/dqB/zTTjN3kfgx/6m5/WvQOiBQPN0I+59NhazvzJb/2dLgNh1z6X3tXvvNLqF/vJi1RXxtrUxVXJV5/v49lRmBcCb+Hbo/d1cpk+P/tL+8a9Wu1tbVbcCqH0//YmWVTznUiZ+pzIxbts0d5LOQvP1S+/YAs1WuRi2DNj87pAW3mV+/d/N+K82sdVPpq46chYy9f2dXBtgGv/uRn5v5DS+10B+rb+5/9SO5XenxwioV5j6WqUbSf8kd6VgSw1i+xgP6j6A/b4RuYTSuaRPGnXMYymyC9GfSvq8Fe9pmb4VmeW5JVNJ/6zn7wfKNYrMV9vAm1R6vgVS5H+/vqrGjPPfcxW/X3Z44bbi8vJSiuwWsgtJk96X1203jpIdZLcVf55B5Oam2laAf4a95PzyXMXtJcl8Wq8rjr+n8fPbjs6qyx8UbZyq/6uO/1HkYt/fksp2oX3O+eWx9a9k3p3riuPvmRTZ7QLvanh+VOupV98sVH1903eBFNlrIjNo16z33xyfv+057bUueZZqbz5v2PnXfUGU/L5Z/SEjKZpoJHcNWXX7EWW6kiI76Zv6vw7Nj/9Qfr9ts44PzfaBFA3j2zaBdSqzU6mpU/KOR4xS8VPu+IbfXjeS8TBKjXfQf44++8iL/6lMn9KdJNOmlIqJf/8Kptz4H8f1oU2nSV+vE/+A80Hcnyy5uv9e0qzQ+PdVE/+SKQPS8b+e70D8A6jL9yKTF3ov6Y2kG11qsppARH/Pbi1YAXgol1DavGXe7XC/xHpxhr+ySPM+L3RRvlV08j6zZBvLI5nyyh9yqH6V8ibzS4Y6Zuzb8toex6SGY0CRBqp36wd7Np0omQhI3beJ28annhVb7CdkOzluazgGFK3O9qaNfT/512JWapp9p+ril8xnu+6ITrBRuVRy9wfAYQPJrhrILR8wre8w0FCTVaIWushe7wGbEP/IYyjXvpDavwrtbBUPrJgH7DOU2fPWmiZ+27565TYR/+YnaZQMgGGX/JHcKvbti3rndq09nBwhb/NrA9A1L2RKpYWkhQJNah9jbJMWJAD7F6PN2+THP7q2X/wXgwRgVMlu71CP5MWwvxKo/R5NYgeE7ed2XevRAKiSTdM+kZlW9rLew0Gh6uiWHijZ5rXHYVcAxyZ1tYz8LUOHNR0DqmW3smPQCpsE3iAn0zWBPrmQ24+IGgLol4dyG4gT/8hm21njb1HfLuf77wJgo/Yv/WEXW7Nt4iR/ZJ/aEnDWY7+NJYGt//dHN/EPoF43MiP6ZmSvjSVunVqQAGw3a15I+qreQ9kiFN1Hjn0nCEWUz3S01XfZvR7zNvmnzlVJm8sm3yz33bEkZ3Jryp9L+rSm40BRwi236+Cnl5EAuIk/SaquT+tEZtjvQtLPajoGFClMfa3zGGzHud0QFD6/3q/j07qTaSleSHqnhudHtfwVOZigW4+gwWm1A5m2wFCmPPqs1qNB0WzvoURrHJs8kvRW0r1GmrEjUMf4C3MQ/3Vq6njAE5lxpTvV33+Etpmnbi/kzqLi65Mqzs8Lmb1MNycAVn00QJNNU7dd/BcZHVVG2oXMK7GTYtaPxCb/Ev/ou3T9X3xM2EesKuV+KBP/RDiAplsqOebqxhjryjFqkwd1H8BIiqTEhhOpY/pGZH57J+lLfaCZHsvkfN9L+tne1zCK3KOHkiaZXnMgRX7VOyv5PbPPZ59zkvH5gvj9TG7l47/+25I/829F5iJiINOQuIn/3cXJmtWec/b9sOuynstt2zSQ9JMGxADKdBm5ZFzTRTeKU4alTeVN14yi5PfZyr+yjeP4lNxm6pKr0ov+fD6SokeSHssM/dkkoKnMZgJ/2/nzoW/6Hv9BKv5njXq9l178p6dMLCVdF/z5PJOiJ5Lek/S1+Ksd8viNpI87fz70Tbr9Hyrw4j9r+7ptktcDUhPjf9d0waLj35wPoZ5qpq9J+rpMCsBA0itJ3+v4+dA8tn1q4vJcSmzh9LLgzyOQouTKXOXGw8dSdCFT8txovb/ig/j3JzKtk1+svd7LKDnFuFnx23TfivtT7iR9qkDSn8jU+qGkLyR9yvtZq3Ec/6ZdPlRyS81y4n8kr8ex1M//r7z4fyvpR6nX8w0peigT/1NJP117vVdRsoOb+M/iu1J0KlO3f6KxpH8r6U9legDfSPoe72etkvHv7581V/Htv6rj/zup+j8d3x/Gvx/IvN5PNtb/kkstIv6z+HYc/28lfaqRpD+TafVfyLQKvs/72WLJ9vxATevfbr5vR27Vz7mkHzf8/Rt7/ZnZx3PRNbZ/e9Wjx/mQyXcjMwXWXiX/sOHv31X8ea9GCBt+vCjXKEomqtI+zsav/+8k/bTh79+VV/8vJV03/Hixi83/WPVub7g+tmtSLyR9Wvn4TLPHz/vHz+eQpNe5Po9xPP5YVX5n09S+AvAg9XXd3errU830jlxztUpVzb/ZtKFw/kes2qZNV+tfAWAg07l8JiU6mtFlNp3AnYuDtZ90WXpr8maxyT9+8q9U7vrJdvvvxzJJwGdi/Y/uOpOLdhML7d+mqzvsp2JLaT9Vs6znC2Xq/3ck/cGppEfSv7iX5jclPSlqdKrkBICQ2G8QP/nXT/y0+ygUbyhJmmumM5k2wNdSz40qDRK3TmQitqz2uUn+UUmPvu5Cpr9i23oaw/g+288/vyVMCzWrC5nzyazUYmv9J3IpWWiGwar9Z/tlyij/k8l/5XtH5hpzX/zbq5R1fo1IDZXVQ5monyZ+8r5cyYBmMOe2Xw+W0QdUdfzbnSa27Qd0Krdcxvb6f7n1t9jNRvm9JLf3x2O5df/RdmZhmUC0j49hr1AkN9YKtA2xf5yHMtfDbWF3V2WHVUjEfV4XcgnAbeBfKXFN1AW7xnztgo12egr6bqjkFHHkUXsCsG9zVb6QNNdItzpVcnWQqqr+7jQxqqgw/QW47ae0fzuhMqVTIOs9GlQrOYzY5O1oy9HsgUN/gxN7aV/2882VLJ0stgwAqmXjr6ruvLncVo1L/wDQUf4EINN5ZBPA+/LRJ1c8bRa//q+GuYL0t2pFn1Q/9c8+27b2ZXryS5L/V81tx7cH07/6rdoJsf515rZn2x3/tseKcxbIr9o4yt/fTK9UebgC6Arbr9+33v38mr1AyDrbj9OGY0XZ0uN5xH9WbYt/YB31f5+UvUwQsC7ewR61GshlgdI3klftCcD+5dzm4tyka9j7+Cs1Hp7/Haa+ZmMH0KtMHiwuwdkfYq86ZXop2/3pb7tcNf/Tn4vmQ7/4n3649pN+aO4rnSlQoJkWqmZoZirTfHgrt+3yWfzztxU8P6o2l0svTcY/6jeJ419KrgRc1gqgoUysv5HZAPwslB7+s0kIfFHC86Fu/lm03gbouiYn/0ou/tN1/7YV0/Jzn/+dTMzb7oRXpTwfdku2xm2qW3kTfKuN/Bu583jTCgb+Nenm871Zu+i0zVTm/b1f/eROpqVvWwJohnAV8/PVT8p9viq8lYvrrzb8fi5zRp5q2wonfvog8Z/VndKrx9zItPyHMlcBaIZk/W9vl/18ZbvxnmlTfC9kaqGBttVG/nGeSpoUdmx9YMvWqSS36v+r+CesKdUl1I7HsDEQqh31oT+O2ZdeHGwz8/qPBiIBMDt/F5w21IfhltvoO+r/Y/gxP63rIDKwvUNMAuqKXX39997tcsaDdrO7i/Rv4cCmCuXOhPzx3/cS5EHdB5DVUynyEzhfrr2GINpUOXgzhLbeP87wz/ieXEVuCHkh6fWGx/e3mrtt3XuezTcj07luB9h+UevrDaRIcius3LbwnEeTpcubWabza+SVZ1L7zs9RHF8TBTLlXNbybRTZ9+648heoz6UU+avXvs54/vp/v5R03bLzP4jLr1Xpl/n4R5H5emz7C6jPB1Lkb0jzecbz96kUncpNRnneuvM/iFa3jorfcWS7gEaaadK6149+CaJAs9XW3UO5q/t7me70afz9qZIbVk213l/xgRQN5RKAP635/P+uFNkNAb+S9OO9x2OvfyQTx/uuf8bR+s92xf9HkduicKq6+xPQdyb+7daEQ7ndBKYy8X8vExFDuS0MFf8+3T5oWvz/tRQ9im+/lfR3B8e/vQqYHHB/aX24dlv8f9uL/ztJPyX+URvbn3wqU6+fyp35NpnXXgvb2PevD9Lt+6dx/Evm+j/r9UPRPpaiJzKv6UbSP+w5Hvt+SHay4e76/6kU2UkeduDXX2Bl/frhG1GyBKX+x3ZX8fW0FI/GcT1ZsMs43hcaaSLbdyGZmE6PX9j2zTD+3sb+VKadxOeDIj3zxtMW2pQfgHxc/I810YV29298FPcnDGXaFHb3PdtP0r7+TjTZVSo/iPqlaKa/Pohj317j2Hp9W//GMP7er/8pn4F2S49/3svV8QNprT/PXv9v2hntkP4DFKv2FYCzutl/F/nJv/7qcpsNVl/jEzDjEfmb9217lnDH77qm2Az9vPzOxfqPBt2zL/Z38zvQ2rigvYupPFvrmr+baSRWOEGb2BVrpeNquxMlpwe1TXKF0bjJn4nf/jrm74H6+PF/suuOO7R7PRtXeh0Xvy55aBI/CtB09qy1Sb62I+tUdiA80ETStXbvHHSvZsX/Q0k2AfCw1vy5klcw++J3+6Nufv1DueFDoF52tTFb79tEQMuWAzb+7d9s4ycMNyH+35X0Xnw7W/yvEoAP+Jt0n8muVz6USwAG6jfw/p1p/WyeykyIn2l//T9VVSueH+ZJ/O9Qps9ukPjJLv57ddjrHYr4x6FsfbxUu/vVmutEdp32STxemh7I9w2lVZKgjflyd3hBn9k6GeXzR/rt92lDmT6Foff9nYh/lCPPKDQOkYxa2/O37T0/U7L+9691iH+g3c7k6nbJtQe2lcPV7O6EQ7UuAXimsZIdx7d7/+bwBsExTYcsf9OHKm/fJUE9XKISCQYokh//zTnfq+IGAOyw6DG4ZEM72e27pT5Gv2Qu7/134JgEXn9YkARgtEfeLYv95L92lh+2e28p0/Y/Nn5tOhXtc7TH9ri15/OJZqsElniNf89cbjZ8E/it+MNa5UO5LsC50q9vO39StEkJ2DwBmyEdtMcgccukI80SKS+vE/dfKLnNYd3O5BIoDruat+t7Sas1FzNrSvozcDybBu/KAJMcP9NDmfN7vf5fKD2Jtt7276nciuaH1bp+es/+8RgiHVU4EedZOWwKz0DShSZ6qGRJ8Txxb3+yBIC2s2v7DTTRWJNEKTtQuv5Pxz2JP0CbmSuDmQINvMUV7cq++/6SNhnQHenVfA/9G58tP+IdgFCh1iUAJ4eodndR75p579jT0c4dzjqAfegQfl+qvqmSaVFAl/nJb9nP+fY3iodyay0c80rs31BeoH1sd9ixlltut8dDJTdBz5oAQPyjve6Vb7+Lpfe1ne2AhzJJQKHM2h4vM/593hIUqN6mfW7s1lfrcbw97WXX6ll1mMptY3530F88lluh707S5wf8TZY1j6dbbgP1sfW1v4L3Qsnp7+spgevneyibAGjvU+8EuDtJb7zb+70j0waQTHx+dsDf+OsA7jOVe28OOyKgKukJwOt1+e70Nxf7g9VP6nQvt8viYdH2nlz832tf/Nv2UXLbz12mW24D6xZbbqMo9l29kNsvxLb/75VOALZ/4ZeNc7lrJaBIxH+5Ak3iOvtcpu1/IdP/P5CJ/58l7j+XaUcsvO8XSu58ABQl3HIbxbA5VTON4gkA6YW/kuN/oUys2/i3y4RQ/wPtN1VyVe+5do9nLNTmsc7uaXwCcCBF9rZpeF4/SN7jMnK3Q0m3D9z9DzF7kLz3R5GbA38n6fmDzX9nvdzz+9me34/i419tobfn/vuMomRC3m3Ox8tq3/tRtX3vP5BHujzK5lZq+flpOwCkzR30H0RuTaG5pM9Trzcdn+O4PLTNCOIXzTXLGb/XrY9/PwHwZsPvP4jcFt4LSZ+mXm+6vXMZJROE8raHgPK87n38+wlAmxIdriK3Svim+j8d3+Mo+X3V1y/osysp8hN61uN79mAm01uwe627iTYn8wVREK8JGt8r9fijKLm5ePJ6+u+l6FF8+5Wkvym4/LiRa3kflgD0ULvjP4jc5GpJep3xeH9B/KMy++Lftvcn2rfWdZb493sqx6n4T/Y//lSK3otvfyHp44Lj/5XcVfym1vw6mwR0Iumrtd9eSZEd9DPJztuv5zenPv+U+Edlxqn4T/fP+fG/m20lpI2iwPvr9Xiw7d/N9f8PvPr/S0n/ruD4fyM3oHdY/D+J/0mb4v+DePzGtilerrenNtzyUf/jcHmvx/3xRil//173+OOlryX9eSS9K1P/v1q79ye5378g1R/AeAC2W69fsgqi5JQezjffbHW9MpO5Avr3kZkELJn6P5kAnDf+/faY1IX+UpQpb/3v8nEsxp98rj1kr2G+GSUnACUTgD/L+XlcSpFNL473T+HzQGOMvOsF07+VPD+vpMhOj/Hv5yfCJifEtuv89scINr3+NJsgnMbOQPVofAKw5IJn+xLRx249v8mpTMJKVevyFL1Bjn2sIt8TANjkXG5wf1N5eSK7FXJ2rAwINNtQLgF405z+M7lVwg/Z7JjLAKA9LuQ6AKcbfm+3QqcuR/PZs1UqZ4UKP/lv91H4R+K8K7PmXlneynXqTQ/6C9tfsvkvTJ+NfR3t3OMA/dGM+LcrBw/XfvOepD/4l/E3vyvwwGIvvGc9bALAULvq/+1rHwPNk17PqniHRML2+v+Jyq//p3Ird+1nVwKVtq3p5w94Ak1HnZXFI5lSyU7wLdbYay/FEzKAEg223MZmj1Vmi8ROg5Ioj4HmeSzX/j9syiDQJbtaCfZKPp0hk2fP7CaZaCxlaKHfKlitIp5+32htVa8VCcBWNSeIvyX1sILnK+NVkfwLoAonMuXkUpsGLNwWnodOqGh7kwiAk2W7bwDt4m/os21ubxaUE6hXmWfgYdGweTNxbfxJse7kjvHw4fzt8e+2+Cb9B+1Qb/yH2n4tXb47ucS/wxOgd7fxiXzACLYsYeKEqnMobCq3Ss9h9b9/z/USw7Zilht/C6DdpnIthunWex2r/AkZAI43V5nxD6DJ/H7K4ntOwi23gSYgaTXrFJ2BZnEScL09HZBakgC8+0Qpuoqw616cyaxuWTb76opccdh2uVNlAqjCiTZ31d3JdBIc2o1HswBoj6l3e9MKv7aDcKDDhgBpswDtcSMXs5vWDMzTQUhZgGrNtXs/i7IFmnhJs+tebbldlLmyrrizL/6B9rBXqvVtSWfry81t5S+k1cq/X5Tw7Atljf+vZPobT+LbSTaZkJocbVD/gLONls3pt6/keve/LOnZ/a/7vZC7vl9fAczf6hRoA87VLH4r1+5/W+eBAAXwx58oCfb7rVzcr7f/81puuQ2Uj/jf743cGGA5/X98Cmi6bX1c6Z48f2mPbtRnxdXQxHn1HtR9AM33N5Fb4fKtpH+o+T0Lou2/G0iaZDq+v5Ki9+Q28buXqdLfxl9/sfcc+ThyWwDcS/pR4v7fliI7mHIn6VPOOXTIt6RIcmlun6+d30HkL3k/2XP+j+J4sTFz2/h4+WaULD1+1vDjBYrzcRyvC5n4X68vTfzbLsV98T+OH8+6bnz8fxi5iVL3kj5r+PECxfmmFJ3JbZm73r7NHv/2dnjA/ev3YWRWK7RJC583/HjRJn592Iz28CiOz1BjzTRUcjuv5xmPb+TFu1REvI+iQJODy5tNx9Ou6w902aUU2amjSzWhPXwVueiYS1po7K3qmTVeRqnyre74f+odz0LS69rfb/RZ8+p/P/5tautitbLvrAH1vxTKXnNkfbwr7/2eqwnlLYDmehq5K6CFpJeUF0BvXEZucZ+lpGviH+iNcZRcwDBb/hFQLXv9Lpnz9fXO8zVIjcenNX98LoiSyxnMdh7vVaq/ZX//39jrvyD+26YVKwDX60LSIzUlPz29hZhbLei4VTMfS3pP0jvx93YLUJvQsN+ZTIL05ud/GN9jefQRAs1lu772rRjU3Y2w/TlO3ZjTBBzK7hOwfd3q42u9dpQBX8nN/CX+0S9nctNfimrf1rf64DH8dUjbc9Roh+ZtATuQifq5pJlOZeL/RMdvgFnkdXGgiaR8kdi89xx9ZVfjbY7TxHcjzRKb4N1mfDSbbF8UG//HsqUb0ATNq4v8iDUJwP4Ev2MezSqi9ezH/zGPd6qmlbcAmupSL5WaoAWgJ650nZgA/bLOgwFQqbFuV9do1P9oPv8Kd/8V8iD11WrPSJf/Cg57vf50vv3Y36vNSADey25i14yu8XSHYaBZnARcZxDufu5thSjQdncy6e9SMed3/VsQZmWbCe1KWwKKcCd3SVFU/GffhrNO93UfAFCbqUwC4KnSqUFWtlLB3yKoDS41YQAQPXIh0w9wqlstdKGZTrQt9vcrOgGwiM1Lm5d0CTSJW/HGdpjbSe7HPJLdQaCoI7OOeUy/PKIMANL8q/NQgSaJIcWs6ff+4HkR8tb//vEQ/wB28a8VmP4P9FOb+iwBFCN5NQQ0nZ/HN997b5v5t2mBr/ad74dd0W9fzCzf46J5SADe62389UxupbvueCMzgGFf2b3cmn77i0fJrRk80KY1kKZyjQNShdA1L2RW0bargG5zaGNhplHqJ/lW9CmfX0rQBYh++U389UL7Y/yQMmCikZLDeM2O/5G3I0EopfYnALrthcwEoIs99zu0/p8oUDKdMOuagtXykx/pBkDRmjch7iL+t5AUaqqXWipfAvDh3ZHlI4bRJMstt+uTLJHOpMQKwFmZqQRNKduAZmle/W9raZP8ey6XBHfM8dm/HagZ9X8ot+JyM95vAE31WmO51s+JpNc1Hg2AKr3WlZLTBp/XeDQAqjTRlZK9n8Q/mizb8hYzBQo2jGp39dp4LrcgwWH9EUO1KV8BSQ/qPoCqPZUie7ouJL1OvQdjKbK/m+lS0nWj36MgPl5rtvczDaIg3rr0oaTPU/f/nhRNZRJ37zb8HmiyII5vW0Gn4+HKi5d4xbqM5/ffRybd914m/edHGf9+FCUbILfEF1CQUao+nGyp363bzPH/k0h6ItM8/rWkf3dE/PubZhH/QFECr30vrce/Xz7ECesZ4++HcfyHkn4l6W8z/v04cukCC0kz4h9oDHd9PJT08oDr6Q8104XM9cQnGcuTKymyrYGpjrkeKdZYimxCY6j1/pHsglV5a3Yroj8BzWXbB3ZIe188PpWioVy8fJbx/P5AimzC3VSHlDfleua1n0JJz3MeT/b+SaA+fvxL+/sH9o0n7PM0rv/t1UD++jafvK8nLf/1FpBHkOpvn3D+FerbkZkQaRcA+vHG/la7CJBZ4MRPGGr2+Crazsb/qkbnfCvU9yLpkUxi1Z2kv0u8v5dePoVb3MBOeV6K+EeZAimaJc6715xvhfrYq//vlc6H8Ot/E/92zyT7U8pjVOnDyE25n0v6vOPn3yhKfs/1D5zerQDsr7izya0CSYqz/ru3Zm2g2Wqw4mbD738Zfw3VlBVXgMP521dum6WTb5Wrc7kEoGMe6ZAjBFAGP2qPi74n0uCP4gc42XnPzewmmwCKtq9Gzl/jPpb0XvxI0xyP4w9KAGiCUXx9fHhknmvqXVNn5T9XU1oFSxV77W9XUKC0Q9PZeMwai3n6ywapr3VaKO/1UVITXhNwqKy9c/5Qdt7na0KszOXKviLaAHaolZ5O1KMJUdVlX5MZDxnI7Jb648Rvh3I9JS4RK8+a6UAWxH+5/lTS+zLv84u13w5lrikWklz2xbH7JQHZzFZJpxIZLWV4X2ZMxNb/SWcy77qLf1v/A3Xw17Wl/Yl+610C8H7nkkLNNNBIk04uaG1m4tqGUfIVvtRIZnszNvNG98zlBviOuxwwW/+aRxnmPBo6J4AqTeL6TdKRdVx80TCQFObtyOJCGKjSTGPvu2O3rLHpPnnrb+p/oEn8RJzDtsA60VTHR7K9HhnI9DzUrehknSBzQjVQH79v4JA4GMj1IyyOeD4/3nYtTFCVacGP58c9wy1oA5uadsj56pcVx/QnNi0mXLJOMcd2JteOatprRb8EmjCqVbh3ZBKAJLMCaNIw8VPbuuJqAFXxp7NRAxXvkVz8rycADuVGTE3yH7GPKhH/5bqQ2Ut885KCp/Kvi/z6n88CdXC1UT/OwWMXKkQf9C4B2E/f2xz+J/G/eeuKB5PUu7uLwyX+2n/pBAgzZ9+taUSXCdrHJbknz99bL8H9uGrxlVzsrHd4HXZkVMhA2SYb60N7sRrGG33fZnzUF/GfD2TKgqzsUCHJv0CZNrff7XZVdtPdrAnAr2SGtENJb3IcFWlxQNP4wwWHJfTNc60FbvcYuoj/1W0S74Dk5Lv+p5RDG4WyfQX72we2T/GwCQNJ0/hv/Y1x62QmSCZ/khfxj7YxUwMPj/+5jpsAYFfcbUo9OUmsmGZ+kocd8m/bWAq6wlzrB5roQoxmFW8pV/qtJwCey7Vx1vcWYEVGVCXUpWa6rvswOmchE+FTbVsB+E52LNaOedhkTOIfZXPT+UaadXJBv3rZ+F9o03jIUP7q/379f+gUa6BId2rWnltVIM6wWQ8SgEeR3wiYeivSbE7BcV1xs4M6AJ9G9gI4nmH8IPn7IHKPK0mT1O/zST7fTOb12ucLNzzfbM/zX+/8/aUU+XN4X669Xvt+2+EMexy2wr8t9PUDvslaPIwjcy4m16IxyX8LjTSLVpFiEgb3nJ9fyDRpzSSBI44w9fiXkbTQpSZ6LLOZlk0E+ErSj9fiC8A26/F/Gcf/UG4Gup/eE0Rjb7rL7d54+69yib/HTACYPEi2Ka4iaaqnmuhrkr4uN5/+laS/Iv6Bg03W4sXW/+dKbj8XysTvOBrpdvXT673x9iu5ja2OjX+fif9nmujrMvH/bnx0LyR9n/gHKmNX9FtImulS0kVkWuS2vb+QiXt7+/bBRMenycykBzNpNTD5t1L0WCb+X5nvK47/fdc/2aTL45EUucmZxT8fkNdS0q0Cmbj/WuR2+nkr6ZWu4mQiSfosZ3ym2xvflSK7EviNpJ9UHv/F9k+mr6cCKUoOBhb7fEBeri9wKOm9OP6Xkm4U6Lneldva/tOC4/8jKTqNH3sq6fOW1//Pt44PSJvHJ4AimfGmWfzv21Jk6+47mZSVG9n6fqC+nY+BFNnbs4PGP9Ju5Eqr32gsRf+jTB/mXNIPEvGfdbEFIC833nwt6Tte/N/ItuilicYy8b97/Ltr/PiX0vkEh7ALItxI+kddSdG/kYv/v1k9ni2BgSq5+J9I+oYUDeV6/99IutVY7hr/ZW/jP14O8Mj6/ytJv9WVFP2JTM/JQtI/EP9olHT79ioyY4O25//zxO+fSqv8oLkOGR/MJm/9a//eW66zV+UX8ulBArBlEnsnGijQ5IBF6O3i9fuG9uxqYLtW4K1ypkG5y5ufKJlCse5UydRqP/mXmQiomk3o989Lt3136E0ICDQ7oIn6QqbRW2xMn8hcgjyM/wEogq2x7Bpbtvaaym3A7db93+/Xkn6r4raxMaXPQGYzvSeS3i/okQHYut+fBCC5zW6nkrJsTPlbuTZ/sfF/IZP8+8cFPTKA7ExH2kimvLiQS/cxq36PdeuGCwp+7seS3iv4MZvEvbdAM7ntaodya3PbxH93NVGGc5kyoKv1v4n99EqAQDO4M3IgE422/p9LutdMgULNSuvN9pfO6Ka+rLqEJvI3rA7lx1pT1uCuz2HjH2lfyLSG7iT9Wn8s04c5LPTIgGJcyC2wIflT+LcvCdYHx49mfCEzJvpWgX6mP5Hpv7jQpvXAgXo9lDk37blu4t+OEfTbcfH/RnYa1Uif6j2Z8nUokxIMNJ/NEVhv/9tSoawrg+SyhPn+ftMxBqkWPSn48PUgAThUOkxMJ3S4Fhzr9z/kgsDf0rd+ozih0Spjzu3uFGObSuFv80PyL5rCH3waaKZAp3E5cEgEX2qiUJPVGZ2/QnWx0YwSBOiL7BF3qder2/GKwTltrhspC4DmGev1Ko04lPS8oMdNlwIMRwJ1s4OC9np2qkC3eiiXElx0AnD62btmtlppSaJPAE2TvBrflBQQrn6zUHm6GPsGA61ormQCcJqJfDawzsPtxgigGY5vb7yQXU/xQ830UP7VEtAs6dFp5PWFTEvord6XWcTkTG7FRKBJ/IV+XJ1HaSDtWsBwl7eyK4A/kkmuHshl/QDNZq/it7cKTtTca/1D2+20ebBJ5xOAzazWLMPpxzRbD33s8kPwQsk1zopOAF56XzcPgNjUCHtPOvtQJ/8s9ZP73XmZniKwi72A2H7+Z2WOYSnTYXYjFy3MoAPyspHqr/4tuRVAsw3mnSlZguSvX5erx/pKZkMt+/2rLX8BIAu/O9qPf7d7R5b4H8qUAcV0cLs2yJ3McJr1YtPdAZQm2XFtyw3bhlis7W9TtFdbbneHv/4KQ4RolmSvgK2VbfxPV4sGLFROr9bdltvdMRTpAWgHm8ZmewjMbXv2lhH/U+92mRMM6nOmYndQAQ7n16lTpWuh/p2PMwWrNs2p9u93mjbWS53J7ZMgmfd4ITOWATTJnVwyjD1Pjf7Fvu/4V/9KgSarfRKWMuuBSqwAjOaZy2WoJM/5hbo87XYfk/x7zOv/StJCl5qsWva2jdXN/gt0ix0H3P7bUM2+Wt23nEZ/SzXs86DuAyjalRT5AfE692scRzb84y1y9jzeKPJXF953/0CKJLfeyG3m4x1H5nFuV9sbDePfTCV9mnq8b0rRMH6uO5mL9BtJk9XWfLcZn/9p5FIibNeon1yRXiF50rlzDugOU544WcuDfS4jt/FaKOk15QHQGMEq/g9r72T1zIv/uaTnxD/QEE+lyE4gjFc4Ljg+P4yS1wrEP4oUpNqvs5rPryBKdtGlj8f0F4zj1bM+zxlvl1LkT3VaSJookJsWvLs9/y0psgP5U0k/3XM83/buf7fx/h9G612Qfj8B7X8UqWnxP0odT7r/yxzvSDNdSHqZM/6fSdGZ/JRl6XYV/+GG50/6rhQ9jG/fSPrRnuP5Xir+/2Ht/t9ItfcXqa9F9y+gz2x/urRa0aoV8R9oplMd0/+f9EH8+kO5+J8kBvd3x//HqXj+yZ7j+Y43nrDQpvj/IHLTpm3MT+WGVuv+fIDmGHnjgaGkyZ74s/W99VnFY7ofxf0FNsL3lRcAdsl2/fKh11+3UPXxP47r/1OZLIBfEP9ADvv6C5M+iOv/VW965fEXRCPNNJR0rvz9l0C7BFHgrZpd/Hj5+vPZWyPN9l4fZPdRlMog3PP4Jv7twijpfMuxFPlZgOW/P8iikysAF5vx7vLqj1si//BnOW6GgVvc+zT+dx7/ZtOqZv5MXTsL0v31MWsb2Wf1N0U0ww8uCTo44nEBVK/s+UJnSq5RDqCJymnvDMQ2wEAz2cE8qaw1+uy1Apsoow8GW24bQbzmVqhiVsw6UbJ2XSSed3/b3k4iPuzepqvwYuc9bLyn05Kz7MoEtNn28zyI9yeTitky215d2zJgmnj+/fH2UNIjua2S97mI77/90Yfe0dh7UO+jfPEE1gbYvkaPH/9FrDDkp9oe83i2/j90JOBCZttvaduKXxepR1vG39P3B6SFqa/72Pq+ri1+hzLjjSdiTX+gSIe0X87l0nXqWHFzKJc5MK3h+YHuObwmt/19dfWmj+Lrl6Wk+xqeH6hTUHkPQ5G9BZsMta9H32df/7b8xaaumgyjkwnAxSr3FE4mxw6UfRMeJ915kO3ID+3y3/bM/qo+9xp5BaO5kLErDANorjI3OAbQbGXX0ZQvQFP5F/JcvANF2N5p5/9mUuIE4yxCHd4KWGpfeeE/WnqIghIGXbc7kmwavF2xs0kOKQOyL1xg/8IvOYAu236eDw661/HyXM2f7b9L4pg3JyD4E3/82AeQtr5Yzv7rATu9rq7a9ERuWh+A/rEj/wCKcegExrqvom2rngRgoF9MXl+Y+Imv7rIJu3UuAbjMrqXDKuSsp7ydvyMd113nnm8uMwvQNsSnG+7tV9LT+L6m0+HYd84+o113YCq7JHhycICiAIA/1EmZADSPvzrf8ROSNptvuQ2gbv7KYeXUzlMxZIj+OSSaipl8Y59p/Yp+/zF8JbdG3/SA53I7CG27/1zJJGCbALTYeIRAt9hzf3vsue3x8k8AWHpfXe26/xgsuwr5QIfF/xvtKy+mSib82lQBrv3RB9We5/aKOlS6//2wY7Hxf6bD4t8vLzavQDiVK3uIfeAwh9XXNt7rWgF4uuU2gPL54/l19KbZ0TxbuwMoxiH9AX7M1VH/T1YTluyY4W0NRwH0Rdkt/emW29sMlezbr38BExzuQd0H0HSBFPnfz3K/Z5dRcm59ejWM28Tjj6TIHxa83fP84/j+tvNvUvFnPJIiuyXIUtI15xhaZRSl1uWq9fwN4njyY9pUs7bhPSv4+C4jt2rHQNLLxON/Q4qG8THcyQwA3mosU6YtJV0T72ixcZRsZN/WfD4Hkd1mJxn7qwTdgo/Pvn67I0Ay/r8tRRfxcXwl6UtJrzWSuxB4SfyjxcZee7/++t8cj9+1n04SLPb4RvH1jktK0tb63044vJN0uyqT6n6/gDJ9ELlNNxeSPiv5fL+My6Nt7ZFRJIUK4km3+/oHsnomRZvW7Vu1Dri+R6985MX/XNIvSj7/r6Lk969TzxdEQXx9cKri+9u+KUX3cvX8TGPZ66ORbivvXwTq9UHcf29T5D8v+fy31yO23X+9cXzAXrG/LiH+FzKxfyNppkuZ1z9XoOsCxkOAKvn92+X3V4/j62nbf1d0+7xsH8bHb6/123b8QNKHkWkpS1Vcv19KUaK0aVn8XHn5B6GKb18A1fpO5La4v5H0k9Lrfz9+2lZ/BlJ0LtdiIv7Rbn8dSY/i228l/ZOkLzTWrc5Ux/l9mRpvTPfvj718wSrya55GblHShdb7G9P8/pD19pSfPxlPaKD8aJDOrQBcluI2xrZDZ5sy+TdvEbp9A9F1JhnP/mXc5KiQTf4teyNxoByDLbfrYZN/fevbhBXJljInG397ITf8Kdk1PW7jgUGg7Qapr/Wyyb/V8tsoSY/kuk8GMisQ3GmiWaJUANosS4u7bLuSf4s30UjJ2L9O/P6xpHOZd+ZG/o4jtpuw6BXDgSap6+p283MGcbyVtS7AVKZ771QuwiXW70ZfncolEFS1gvX29kjgXR+UEY/38bOfr47kdtVGMF+p79EnJ3Lxv7mPrFj72xq2VCgj/u3a4UPZycf3Mq/7LE4Gvt7+x0DjhFtul8Mm5rfZQOYaoPrRRKAM1V2/t30s3E4slJrRGwrkc6bkBP5ytb3+J58H3fJQ0hPv+6+UXJCzDraE2FTDbh6HL4+/j88hNf5Q2jH2b8clys1ZwrFIAD5AOeF3WHM6+3Pb4TqpjiG6UO1u8ABO/Ze8fvwnt/Qrq1GwOwHYXj4tZQYF7dyfJrxXQH7p7arrtTn5v+wLguXW5ziTSwBexLfPJc00r+C4gKr45UB9As28i+cw8XOpjA137HDf5td+LhPzti3iVgasv6wEyuenvlaRALi7PXLq/bSMCLTpPqcy7X5/LYL7LX8DdNdC7tq46v619fLGTsZJ9g0UZ6pk3JvhiIkWolMffWR360uviV+FzQuE2LV6DtkyOKup3N4+p5LGmuk2kRYEtE2ZLeZusT2BVachAOWo+vq93dLjj0C72bY7tdkhmjMSChTNpuefsphFbKRJcv/zOg8GpSMBuHLzDT/zOxWT/J9kK6Tq6KA0WwGPNat9TgVwnGY1ddPrFZhO/jLn5NqLo81lx1KutFp4t7mgQjdUu0LIocoa4N/8TP7X7cdhhwEvZFYBn5AQgNZrZvxLbrU/v6YtPgFYcnX/5jr9JP43lJlPba5obnVL/KPz7uRSX6u8vg4V6HYt3ocySTru2qDYEmGmQKearXYiOZVJBlyKib7oozuZiJOqaR8sV18DXW+N/zITAO2UoDO5yQALSXPNWP8TPXMv1xNf5fVBqJFu1wbkzmRitKyJwbalY1cBO5M01kS3JFGglaq/vrc1eLN6Ew5jS7oTtfP4gaTFltvlWG653RYLsQIwumQafx14t8vThZhpc/sFSHojd916JzvFzexgXcf+Fvuiyh9xL78FkX3/c78NNV37LWVGsz2o+wCQ1ShKzsuZ7PwMAynyG/CTtc88iNxqYrYT0Q/92z3nyIeRSQOQTAHwC84ptEYgRf73s5LLxFH8fLZirOL5bEQnVwqxZsQremvkxb9Joik3HsdxPNrnu634+ey/mYJ4dVHagOivquv/OuLf/z49kYD4Bw5XdXmR9qEUXci05+eSPiF+gcrUHf/flKJ35IYvf0L8A5Xx4z9OuK80/j6K63+J+h8o22hD/5nrP08PkYequjwYSdFkLfk/cYVP+QAcaVP/+WRtnNxfNKPqeEvnA4SrhQKkTeP9AA6V7j934+eb0uPqiP9x5J7bfLXxfyrpmvgHPN+MzJJVJzJX0FO5RT3m2p/vtlvd/YNpH0rReXz7XtJnlAfwsAJw62Sbdb9v655gbcWQTRc2uzySSwBmWzC0T5XbXAy8r1U9n78at4nQmdi+E0iqYj0bu+lIVTY9XyjFyb/FrxgGtE0d9X9V/PhPz+Qn/oFsbPKttJowVPnz+8cAoDoP5Xq5FpJeVvz878j1tgGolq17Q0nnNayAbet/AOXzR8Ns3A808xKB622Jrz97qOSCPgCO5fefucnzA62PrNe51l0y+Q9AMfwId8m/p0on3ddr866d7NAFpL2Q28/GXxovnSnTDecyu4RJ9ZdSaB4SgFsnWxgfmmi4uTPjkM4N2yVp1xcFsEuVXYaD1Ffb1HGdhACqEqraC3O7Za/kVg2savIB0AZVbmZb9ca5tqsj/fwTBgeBzE7lugnr2NJz34ReAOUZyrWn6xhg84csaMMD1bJD/1W34y3qfqCp6o7O5Oqf8Q5fAHJayk74Hcm0AtLT6uvirgLsmF7dpRDQNcnk/02JgnXlvmzvCai7ZAKaZhxP2XUr+Z+ri4m/Fv2E2IUE4NbJFs6HNEuSM4Xt4x+6VqFfeFLUALv40VXX87ICIGD4A3lV1F6hXBJuFdIJQydKd2YAqKrlWnULOR3/yeR/4h/Iwm9H19HB7k/guavh+YE+W6reJPypiH+gLv6AWh293VPvuYl/oHx+vK+v/OtfEVTfKmDEDSiXTf6drFb/PJNrhdvfSk3rT6NsAPLx636X/O9Pw/Xjvw7muTet/E38A0lDJXfyuF39ppjp/H4uXRNW4/fz/6Z1HQQa60HdB1C9IEqlwRX6HoylyF+hY7LnPQ6kKJmAu/t4gvjxLdchse3v7etd3Tvj6/0gchc8A0mf9PCcQX/YeLH//IbBUtL1zvN/LEVSusPAPs5c0m2l8XMpRXYVQP9yZfN2JtK+1yf9fSS9J+mRTLkwlxmKeCvpnyT9gPIBLTby4t/We5Kr0V8fWJ8P5FYKsI8x1/74KtYHUmSn6IQyFwFT2fgfa72M2/36pM8jPfwjUwRI0leS/nku6Vfxv39P/KPFbP2fjl2bur+v/r6K1gcD7d+GB/x9sT6I63/J1P0Lmfi/3Vj3S/vLp/8t0v/tgYn/e5kq//+7lPSPkn4p6e+If/TYR5FbI3Qh6dNC48HGs43lO0mTVT0u5W1fjKToVKaj1LYb3ki61Tck/aV0+pfSn0t6GD/5ryR9+d8k/VzSbyT9mPhHj33Li/+piu4v+yiOT5sAeCNpokuZtsr+/ol9bH+BX4K91kjS1yX9G0l/Lr37+6Zw+ErS797I1Pu/ktlesdjyDmiXb0Ru082ppF8UXv+fyV3L38i/js9/fXGZul64lzTTpaQnkt6Xafi/Ez/fVNIrSV/GX+8kvST+0WHj1HhaOt6eefE/l/R5y+NhHI//2cTHz1r+eoA8nqbi+3kqHmy8WPv605tuHJmvC40025vPAHTb01T/Xrq9O4qS309aHi9BNNJMQ5m1Uj8n/tFiT738OdO3Ve75bPOBzHj7paQLJfsHyr0+uPJe71zSNfELTw9XAB54X4ufI5P10WcZt+L1h+wPO/q8M5NP5DY+bNYMR6Ac2xIAD//rJJtIVP1WA/5gnrV5BUA7gLjPI60SgAcPpNCuiTRVl7dSQJ/4se9v+H3MNj82mTBUHfFxpmR35TL+6todfpLjId6X/lTS1+I/eytpeCb9+msyw5FAm/kTf/yJQIfW/37ysOTqVDtZplp26E4ypdfmrYszzH7+kwfSv5X0xzIv57eSzk6kn/+JTEYQ0Gc2fVYqalUB341caWQ6FUeFPr6/ltGpd9uUJO+Zuv8vzE19JZMI/B/+QHpr0xGBPjuV6eQvh40wezUyKTj+pU1Tn0OZ4b8n0r/6fTMB4EIm5+8fH0tfTmUKA+IffTeUq/+Ldxf/c6uRjlRGv/wg9dW8pscyDf/34t/Y1oidijQt/DiAZvGvqKd7ft8F/hXBeZ0HAjSAHQff1h94TJ9hk4UbbgHYrso9Rcs10qzWHY+AIvkRWf0OfidKjsiXj12CsEsPE4Crc3ilWWaDIdxyO6v0SoFAF9lY7Ma5fi6XymhX/0uyrzdLcygefgxt4u9CZq2Q6hOcgGJ161L3Qi4dYSrTArjP9YihtDxZ3xcR6IRN3V3N3OLvEDYd6UQu/qd5HtDGu99cGCh+hm60mYDjlVsZ3ia2H/afb3Nq/zG2V+kbrhEGW78BULDXiQUD/LVMjpmcuC4d+4PEbwamM8GOYQxFyAMJ5db/1xsXDCkm9tPsJKDJ6jXFVxMPT+LJvw+VLAToCEDX+QvhdP96N9BEs9WE5vImNgHt4LeQ99V3XagPXd+nWcHwus6DARogVF8GvuwSSHbHIaDN5qr2arXuaUDrE4Rv6zoUNFAPE4DtQFk5YZk93baoHvTNj2Nn8NhjmWR+XLtmoE0hBPrADnZnnzO0fmmwTH2tjt1wYCCX+OfSdNPl4CEl1qv4q13tZyGz+s9bsQIQusPGQp6kdlsSLLzvq/Uw/ieZoQu7Vo+zVLZy6VfSz/9PZtfPQfxgb+1SoG/yHSzQCOk6MUsb3ZYX6ceopxtgKLdh70Dm6NY78jLE/2//u/Qf/oX0hcxLeiGz+7f+ScQ/MI2/lnW97K9AtEhc3xexGuhMgQaarSYLuhSHqaQvpV/+a3NR8UimIPm1pLdfmd/R/kfv+bXrtITHt6vwJeNfspMD8rFze/wrlkAzzTSV9Fb64l+7WYWvJP3zPL7xVqwACky922VMiHfXIoEmibUGJwXF//ozhTKvZWG+rkZRp/E/uy4xCwCg6/z+gE39An4MVN/fX7RTKb4akKpcuQxoJv+aflOfnp8c2P74H2sWty0uJT0RCcDot6n2pxAeOkGg+eyepVNJE11Jel3fwQA5VT0St54H5Kcgk0+Hej2o+wCaJ4jMVxukk0rfo0CK0s2LmQIF3lL8/u9vC/4Mx1JkH9sMKA7l1hGVpE85Z4AVU14EmkmSZg0rU78rRXbI8F4mReet7EpC/vDBqrTZc/wfR6ZMsNuc/aBRrxeoVhDZ2JeaF///Xooex7e/ksnbeyHpuQK5GPYvU/a1d/4qMluAvhN//39t1OsFqtXs+P+uFL0r03q/kUnVeSHp5dHx/4NIel9uWsHTRr1eoFzPIhNNNk1uKmkRJ8mNVHV/wZUU+RN7Znoq0z5fyrT0iz6eH0fS12QyAO8l/e+Jf/TIB1FyWGwqN7X2VNJtpfHwTIrOZIYVbmQH6R7KTfcrur/uP8fxfybTm/CM+EePPPPify63A1aoOuL/aTxeYFfoMok6Fyqvv/4X8fX/QGbSz/+B+EeLjKPUCFqrzt8gHp+zPffr43/jyO35J0kvC359n8TxL5mehG+26v1D37U7/kep/IDJWvyPomR/3r7xvKx+kor/j1v1/qHvRqn4r7a/Lq+R196XNsV/2eMRP0jF/1+16v0D4AuiZFbhvvbQX0fSu/Htt5L+lvhvsR6uAHyoembvbJpT7FfoUvnzi+wxmO1/BjKDifk2Dge6KB2bTXMqN29/81qGJvl3FL+O/SuE/zb+yt6fgI3/urf62OZMbs3AqbbHvzeBYY+fS/qlzNqAlAHou4H3f/PYLbz8S3zHXUkcHv//i0yLwiZBAn3ib/+bbFGb6+Vq2am5djF+w9b4ZgPvYv0vkv6TmtviAcrkt3vPZJL/Bgo0kTSrPP5tqp+L/4GSE3SL9p8k/Sa+zaag6Cvboj6RW8mn+hV9hnI7e5hS6USmRBiqnPb5C7l6/9WuOwIoQXohoO33KOvZh95toE1snd1Ogy231+9V1rX5e9L5H5qbpASgdcqsG8vn7z2wK8LLe4Vfk/T1+HZZfQwAqpG1pHhPbgJwe9tRMEgAbrGyKnnbsDCPv/C+Y8AP8J2qzg2+9/OPbaFNxxkmkhj3Get1IgWCDUHQZ/s2A6qb3bhTMgnAtjwwKxaaOt3fRnhfAsOlXiZSIF4WfLxAu4Te/81j1yizqwSut1XC1e4ih7jU56u1xQaSnhdzmEBL2Fa0fz1c3yQAvy5ef/7iO+hG+iTxXLeFPwPQZEttG/iqI/6rf85fyr1+MgDQV8ltvm0buujpNvtUn87wQuaKQjIrAAGoip/8W8/w+1Ru4s+0liMAAKBv/J7HesYczqRBvOhneLb7rgAaLmsp4vc4tHciBQwSgFuozKSj9GOmkwOr7uAEmmwol2TXxCSgG7njupM5TrcuSPYj3r6aINA/Q7lu8JkCHbKGZpXeKhn/d9oe9YfE9FBuVWGJBGD020gzhbKxLzUt/u9kBgpP49tTmfrfTAAYJe57aPwP5eKfBGD0y502RcpMQS27gdi2vLv+COVaJMWvAHghE/s2+YAEYPSLH1/Jq+k62IFAO7nHHd9S5axIeiOpvoRHoH7z+OtC0kKBJrX1iYWpfybu595vi/ZKLgGQFcDRNk3spS9amXuEfiHXrnhTwuMDZQq33G6Hzbt4pu9RphfxvL+BuQ20Ttl7aJen/iN+4R0E8Q+0mT9m4cYwd3mz5Tba6EHdB9A2l1JkV93xO/3sANzrkt/TsRT5FwDXBT9fIEX+9zPOEfRIEMeXbeOmz/8Ppeg8vh1K+sWe+Cg7ngIpchX3QNIk4+MHkb8CYHpywYT4R4+MvHg1iX3J8/+bUjSUG3T/pAHxbx43UN7499szdkCR+Eef7Iv/D6XIrvo/l/R5zfE/jh/fxGogaZY5/iXTEWCTev0VBoh/9JltX9uOsqzxm6yfpf3xeRUla+HXiftfxo9n+yBeEp9AiYIo2dt3fVT96uyL/6eRm14TSnqeuP9V3D9h/+1rfwDI4zJKrrP/suT4v/LKm3Dt+S6lyE7Cl6TnxD9QoHHk1tcdKLna/3p93HZPvfGOuUxun5sYHCp7fwLQZqO4/W2XubBTXe2Euqz1f7M98+J/ITN1cBLHf6AJ4//omcCL/00jYq87FQ9XXj6RXchsEvdVmkVCiH90l72elsz5nz+fzl7vuywiP7+G8TQ0CSsAH8F2C/jdglI5621sc6Jy1h7xZwHUsaIRUCc/njet6DmQuzSoMt53MQ31Q2bvbP5baX1TY4lVftFvm+L/TGYFzOMeS2uPV5Q8ZYC0vrIAsY8+2re7xqlc/B8aI8m4LDb+/bUM4g67XI/F5j6A48fuqbJHr5uYcyg/6WC9FPL7HOpfDQTounQCUFZ+jXrI39vBx83PdyriH6iOn26bJ/6z3P9062/zHg2AXWzybzoJyKbIdos/mSjZB2h/yhgg+sRP/vX3uutmbWvHMy1TwoWSTjXTWOzxg37xa8T0BIDulQGbpjjZ35iJQOzxg+6yNXwoF+nFcMm/QFORAHwEOxfILzBOVG3zoOzmCAUXsM5ttVf35p9J3gplR7OviwEG9N22c9+P/0PjI09ibhbHJgDaFQ4XSs55Bvps0wSA4x7DH2IrVv5HNNHur3Ja3GMDbefiY3BUWWBT9uyU3UM61A/b6LNJ1x9Ad+W5IvaXCojX1C+AXbELQJnytt3t32ep//frZjoiULf0/p5+O7zbV8Tu1e6//gC6yca/P9VGSvb89wVlAPrGnwCUTgnsR/ybccSRiH8gm215c5OSFwADjkECcEbpTne/a6+K5kG45XZRbAFG1Y8+2hdT0/jrMUMCZcdUUY9P9x+w2VcyA29NWgG8CKz8D+yv0++kVaL8YQPw/pp95bCxe1zc2tp+sDZRgXIAkPxh8ezsGgOHthjm8d9sHnBcel/7MRwB1Clv3e3vGXJIyr5fRqxHeOj9YwIAUAUbccem3A6VrcfAj/LNv7EbkgMomh3Vk1wM2g2yu8X2ZUjp0oZRAPTRpqR/W/dP6zigUu2/2gD6JB3/tqetm1Pu/InEcxHx6Bf//C+qP83fMyCUUsn0jKmhOR7UfQB9M5IiUyiMZToGh3JFxkLSZ7k+k2dSZLf0mEp6ueczDqTI3h5ImnBOAJ5gFR+BZmvrAlzXHi/m+OyxZY1fP/4laVb76wGaZOTF/2RtPb+mxL+0WgE44/EEXvwPJE2If2BlFLnpPqFGXhtAqj/+AylKJu7Ojox/+4qIf8Cx8W8tVm1t6ZD29rPIJQDOJX1ecXylj/+W+AYONo6SKTLpxLx99e0HkXQhc8Uwl/SLiuMviJI9FsQ/cLincfvYJgDY4cKBTL3+ek88XcXxb68h8vXvZ+WuD2wZQPwD211FyV079sXrx3F8LyS9UdH1+zgez7Olx+u1642x138Xiut3II+nk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j5F/vb9yVWJ/1X9rKgTgC+T/ML7xQY9K+Yfr3f8T8YLry/+b/fjok4Cvkjya+8XA/JpUVbBT8rFL5/d8fm3fYH++FFRLoJLyoSi34p/GIyPZ/u/Sbn/+4X4h46o8t/OMk65n77u+QJojwrA3Kn9ikLAMI1SHyDdbdp49ijJqw30CNiW/dTXYLifZvwDu65aAHA/1dZC/UCuW308WcV88h9320+dkG2GgU0bZZMjhPhfVfPvoSIg29A84U77HKkzVE9SJwDeRzNW7C/DbjtKvQDgPsQ/9MdhNjkfCTxcNcKOM8mk1Z7A+kkABqCjjmb3VU2/u40W7oFd1ZwcuXvC82r2E/VFkYHd1Vx+ePf431wAJP6h665mN5cEhOGprtWRWLIDQ9O8aKr4B4C7OcMFw2IBALThOJOctd0JWCMJwNzJbgbQjqOUl0ZqnihYrvks2y3YdUcp4/88q5wgFPvQB4epkwPvF9XqCLLc1ZL2urhezmrepnyvRpEAxOY1E043EaPifzXns/tpow2bVC0m21T8i/37ay76OW+rE9CSt0vaQP+dL2kD/XexpA20rbqi11j9X3por+0O7LqTpEjqab83d7ynz5OieYqgOq1dTRlOVvz5l737G35SlEk/1f/yMuW7VN2/6tn/F6j9tEjeT1nH822SX4h3GIy/mMX/fsoJ0X8j/mEwflLUVf/Pk/xa/NOaj5OiecHwLzp2vD1emA+4a/6gf06K8r6aRZkM7P/PJn2aFNU1KC6TfNax+DqezT8mQ43/49n/v5qBPRvY/59NMv533biYr4om/gEAAACaVABeg1UuxDFq3C5T1re6f22r+mf76zBlAsBByndlf/b1Zhvop/eTfJAy1sU7DMuHKeM/6fueDrDo6ewG7TtM8mTW7vpo1PX+bdaw//dsxkHq5ShdN9wIGC3cw3o059t9urrIXwUAAADgNhKAH2m6pH0fzYuFuXBYZbTQPlj2RKB3RpH4C0NVnXJtXhcBGI79lJdohnZVe6M+jV01jRQtGCoxz3A1q/8CAAAAwCIJwI80TXKWcZJknMmdz79IWVWomrBrnli8z1T2YxKOd8Pl7H6aOvm3SgTu5/8YqFwm+XbWfttmR4CtO099fYRvb38q0DOXSU5THhmdttwXhm6ackRK475rqhTY4R4dN5dRw/pcNtrnbXXiFs3092HGv3eAzblc0u4Kn/gm7wYAAADAor22OzB0z5KimrYqk4mH/jd5XpTJvtXFVw9Tn948T/Kbgb8/sD7jpGg+nmx9+9OM91GS3z7y9/+kqC8hfprk17YXsER34v8o5f1j4/VnRfL+rH2a5JfiH5ZoP/5PinLsr/YBPnvk7/9xUV+0/TyOF9iknybF05R7rudJXs9u56kWBk98/qCn/iIp3k9ZJfw05ZK1b2Zt8Q/99uOkOEq9ZLXaBlxG/EPfVMfL1XWq3mz5ePk4KZrLXZwvhO0Zz+KvOme/7fky8Q/tac6XT1rYvz9emK8X/wA8hArALbuMujXzzme3g9T1kY9ST7kA/dFM/r1Yw+sdpU4AUhEEuu1J6nhdR42lp6kXAIh/6Laj1Ff6uLrlefd1GOM/2/I05XKT6uj0MvURLNBv7yf5bur4v4i69TAURyn3AaoFQNUV/rpYLRhYD2ejYHhGjfs2ZpdGS9rA5rnWDAB9IAG4ZXYk5o1zllGSs0xT1lWpbol3C/qmmkqdxmkTGJr9RnsdCwCmqfcT7C9At41SbwPOW+wHAKzmphPxTs5D/0kEhOGYZJxxJm13AwAAAFYiAZhOqSZUx5lkkoPMr7lcR4IQ0B3TlHFd3T/WeeN1z5c/DeiAq6x3AcB5o60WG3Rbc9xfR8L++ZI2rN9pyhFsP3XlX8tOYBiqPczF+J+mmsMC+uq80b6c3arxX/xDP03TXuK/4wtoV1sx2Nzu2A4AALCqvbY70D3jYv4CH5OtvkfHSdF8fOZvBAMyLuYfb3f7A7TpeCH+z8Q/DIb4h131UVJcpE4Emjh+h8H4JCnepk4EFv8wHB8lxXnqRQDiHwAAANg8+UQspwLwgublfUZJzrb8+11ODACGyhp/GC7xD7tIzXkYrtdtdwBozXnq5F8AAAAAaJsE4AVPUl7Or6oB3EYC8P6sfbXl3w20a7xw0UCXEASAIRgtaQNdd5bjjHOWUUQvDM3l7F7sw/Bc3v0UAAAAANia99ruQNd8N8kHs9v7Lfz+Kvm4mQgMDMNo4QYMjfpBMExiH3bXKJOM2+4E0IIq9o3iMDzGfgAAAAC6ZK/tDsDtjou6PU0y8ZllY/4qKb4/a3+V5J8+chv5cVIcztoXSb6wzYXO+uuk+NNZ+++S/OyR8fppUhzM2udJfi/+obP+ZVJ8OGt/neRfrGH8rxbyXCZ5If6BnhgnRfPx5Nr2bVyUSxkPkhzO7vdTXt/oMskL20PYUavH/1Hq66tdJPlC/MOOun/8j1LGf7O0wTTGf3bZ3Z//1RwvvN7Ztdc7LuYfn4kfaMm4Mb83jfiHIaniv1rw+9j4v3t7Ih8GdtU4KeYXCXc9fu1v9Nl32u4A3E0tVLbjB0l+OGsf3vbEezpM8mQNrwNs3p8n+S//Qdk++H8e/3oHKU95A933/SR/Omsf3PK8+6pOewMM201JQEC/VdczO0idCHzVao+AbarG/GoBUFIuAIDdtu49WXvGsBtGS9rreM3lVxEZ3fpdYLvWFZGb2J4AwCIJwHScAx225yjJ/t8r20/+uJ7XnMbOPOyCp0nyYZJp8nQNCcDA7jjKbBsQifsA61UdDTkiAgCAJme+gOtsGaBLRCQAu0QCMJ12nMm79uySCLAxl0mKWeLv2zW83nmjreYNdNt5kv/itG6v4/Uql2t4PWBzLlPH7DritfkaJgmBPplknPG9jsqnqbeGV42vAbvqfvE/TRnzlymrf05nNxVAYZetNv43b4kZEfpkXXuz9ophNzSL+4h/GJ51xuvd2xNbBwAeb6/tDvTPJ0V90d/zJF94j2FH/DIpvj9rf57kPyT5v5K8yvMkT5L8fi6enyVFNaV9luPUl7irTnidif9b/aioay6eJ/mN94vW/N9J8Wcp4/l3Sf5dkv8zyct8lDK25+P/eVJcpT6tdZbxu+8dZ5Iz+1h3+GlRbleTcsnFr7xftOavGuP/l0n+Jsl/TPIqz1Lu17+4Nv4n109tJ+VEnvi/y4+KcrualPH/mfeLHTYu5h9PfJ4fYZwUk8Y+lfeTbjteiH/H/48zLpoJhhP7U3TauJivbi/+H0f8w3A5noLhEv8wXOIfYBNUAF67/dSXuLRaB3bJ5ynTdq+SfJPk2zTrVNxcsaKa7h/nbKEWhsvc3u1odvNe0b7fpa4A+mXKbUBZp2oxva+2P7svR/2JUX8lT5J8t+1OQJLkq9SR/nWS0zRH/eWRPVq4tw24r4OU4z/svnEmmU9YBYbFXsC63K+6KHRFNfefiH8AAAAA2iYBeO2qy9qNUl/qEtgFX6VOAHydMgGonMa/ym3xPEqZyjLKWaY5myUBSGq9m5MldMfnKeM+s/vXqRIAb4796qvNZT9JfYHbs810E9iAP6SsQ5uUY389/t9+Qa5RyvrAzfi/ivi/H/tJ9MMo80lr0tdgSIxlMFyjJW0AAAAA2D4JwGt3mjKFcD8SgGG3fJMygpMyit+++87N1X+bCUBHaSYATXKR5NX6u9gz56nfxYtbngeb91XqBODzlNuCMpn/5gTAy5Qxv58y/p+kHvnfRvzf7TJlnfWkubWFNnybegHQ+exWV/S8vg2o4r9aAHQ4u5/Oflb832WaOu5v3seCXVFtB6othQRgABgKi9rXRQVwAAAAgMfZa7sDXfcsKZL5qbxm++yR7+E4KZqXD37T+t9kXMw/nviMwD39JCkOUycA/bb1eG7b4vYkqdMk9pO8HPj7Q5/8PCmOUsb/aZK/Hnj8H8/2n5LyPSmTKZupkl8M+v2hX37SiP/zJL8R/3PxnySTHKfeBrwY9PvDsH3aOP6/SPL7wW0vnhXlcUBSLpt6NbD/P0P2o6Q4mLXfJvlscPE/LuYrpZ4N7P/PkH08my9MyvH/i4HF/7hxfJAkk4H9/+ExjhfiZ/XzkX9ZJO/P2q+T/EL8wY5o5g8kD4n/f7YQ/38l/mFHPH7/+adF8nTWPk3yK/EPW3LcGL+neXw+ofw9VqEC8D3sZ/4UVVIG6zrW97tIGPTH0eyWiO1k8XLI49T1EhMV0umbw5SH0+vaP+iTOvm3in8VP+mXw9ktEf83KZN/mymPMFxVGvxwTRfuYTgOUs8XDFNzPsA2gGGplsIPVX11lVGqJcLAtryf5IO2OwE8wOPPMb6f5HuP7wjQmodvB56mTgAGtmm0pP1Qi1fMcTTNbSQA32GaOvl38evl5NXjQqw59f1mDa/3eM3NkAl5WMU0dQRJAK5PbtRbEif86K9m4q/4r12P9Ksbvwq7bJryk33TMcMQNSO8Ptm/+B0YpmYUDHN/obnHZJsAwzLMrR6QzM8HAquwxww8ni0J7CL5BkCSPEl57rE6u952NiHdJgH4Hpp1KjdV2e8s45QrcboUsnYpYBXnUd+yqapuVu6QTDLJQaRH0VffNtrnbXViJ6iRTP+cpk4AVt+2qvi7uCRCwh8k9TZilKEeL5TbgKpyQZdmP2DT3jbaw4x/GK5pynmC4R4vSACGh5pfVFt+ZTWvl7SB/hP/sOsevhddnbFI5mcjgE1rFgxcx9mwo9Q5NxdJ3qzhNemvvbY70HXHSZHUQfpm4+/ZXxblZTmScof8F1v+Gx0X5f27//Hc73+eFNXOxjTJizvej58kxWHKjdHrJF9knPKSQ0ezV7hIuRNymePZ5EW1IZxVWPYZpTXj2ee9cnbt834yi5fLnGRybfvwcVKcpzy5N1uR4/MMO2K8MP4/JP4vU45y0xt/HuiqKv4r18fvan95muNMrsX3R0lxEeM/DNFJUlRLXdZxPDtOijKp/iDlMfTTlFN+Vykn8D/b8Pbll0Xyg9nv/DrJ3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alt="overlay"> </canvas> </div> @@ -304,7 +304,7 @@ dynamic html viewer $( window ).on('load',function() { // Create brain slices var brain = brainsprite( - {"canvas": "3Dviewer", "sprite": "spriteImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "overlay": {"sprite": "overlayImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "opacity": 1}, "colorBackground": "#000000", "colorFont": "#FFFFFF", "crosshair": true, "affine": [[1.2000000476837158, 0.0, 0.0, -73.80000293254852], [0.0, 0.9375, 0.0, -119.53125], [0.0, 0.0, 0.9375, -119.53125], [0.0, 0.0, 0.0, 1.0]], "flagCoordinates": true, "title": "SVM weights", "flagValue": false, "numSlice": {"X": 54.604099624538975, "Y": 102.4959151770787, "Z": 123.25624795006208}, "colorMap": {"img": "colorMap", "min": -0.019018568098545074, "max": 0.019018568098545074}} + {"canvas": "3Dviewer", "sprite": "spriteImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "overlay": {"sprite": "overlayImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "opacity": 1}, "colorBackground": "#000000", "colorFont": "#FFFFFF", "crosshair": true, "affine": [[1.2000000476837158, 0.0, 0.0, -73.80000293254852], [0.0, 0.9375, 0.0, -119.53125], [0.0, 0.0, 0.9375, -119.53125], [0.0, 0.0, 0.0, 1.0]], "flagCoordinates": true, "title": "SVM weights", "flagValue": false, "numSlice": {"X": 54.60410718699464, "Y": 102.4959481687527, "Z": 123.25628128296788}, "colorMap": {"img": "colorMap", "min": -0.019018566235899925, "max": 0.019018566235899925}} ); }); </script> @@ -335,7 +335,7 @@ Saving the results as a Nifti file may also be important .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 26.481 seconds) + **Total running time of the script:** (0 minutes 28.118 seconds) **Estimated memory usage:** 916 MB diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_different_estimators.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_different_estimators.rst.txt index 461f1cdb3de..44619048d72 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_different_estimators.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_different_estimators.rst.txt @@ -166,21 +166,21 @@ Training the decoder The provided image has no sform in its header. Please check the provided file. Results may not be as expected. - logistic_l1: 106.13s + logistic_l1: 100.23s scissors vs all -- AUC: 0.00 +- 0.00 - face vs all -- AUC: 0.17 +- 0.37 - cat vs all -- AUC: 0.45 +- 0.46 + face vs all -- AUC: 0.08 +- 0.27 + cat vs all -- AUC: 0.53 +- 0.45 shoe vs all -- AUC: 0.00 +- 0.00 house vs all -- AUC: 1.00 +- 0.00 - scrambledpix vs all -- AUC: 0.98 +- 0.02 - bottle vs all -- AUC: 0.15 +- 0.35 + scrambledpix vs all -- AUC: 0.98 +- 0.01 + bottle vs all -- AUC: 0.16 +- 0.35 chair vs all -- AUC: 0.00 +- 0.00 ______________________________________________________________________ /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. - logistic_l2: 334.93s + logistic_l2: 308.66s scissors vs all -- AUC: 0.90 +- 0.08 face vs all -- AUC: 0.97 +- 0.04 cat vs all -- AUC: 0.96 +- 0.04 @@ -194,7 +194,7 @@ Training the decoder The provided image has no sform in its header. Please check the provided file. Results may not be as expected. - ridge_classifier: 17.14s + ridge_classifier: 18.27s scissors vs all -- AUC: 0.94 +- 0.05 face vs all -- AUC: 0.98 +- 0.02 cat vs all -- AUC: 0.95 +- 0.04 @@ -208,7 +208,7 @@ Training the decoder The provided image has no sform in its header. Please check the provided file. Results may not be as expected. - svc_l1 : 27.89s + svc_l1 : 30.74s scissors vs all -- AUC: 0.92 +- 0.05 face vs all -- AUC: 0.98 +- 0.03 cat vs all -- AUC: 0.96 +- 0.04 @@ -222,7 +222,7 @@ Training the decoder The provided image has no sform in its header. Please check the provided file. Results may not be as expected. - svc_l2 : 81.87s + svc_l2 : 76.74s scissors vs all -- AUC: 0.90 +- 0.09 face vs all -- AUC: 0.96 +- 0.05 cat vs all -- AUC: 0.96 +- 0.04 @@ -440,9 +440,9 @@ Use the average EPI as a background .. rst-class:: sphx-glr-timing - **Total running time of the script:** (9 minutes 59.447 seconds) + **Total running time of the script:** (9 minutes 29.549 seconds) -**Estimated memory usage:** 1329 MB +**Estimated memory usage:** 1294 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_different_estimators.py: diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_frem.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_frem.rst.txt index 361d423d538..3da169cfaa8 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_frem.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_frem.rst.txt @@ -247,9 +247,9 @@ FREMRegressor object is also available to solve regression problems. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (9 minutes 48.706 seconds) + **Total running time of the script:** (7 minutes 40.233 seconds) -**Estimated memory usage:** 1965 MB +**Estimated memory usage:** 1952 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_frem.py: diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_full_analysis.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_full_analysis.rst.txt index ea18e7a829f..750885b17b6 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_full_analysis.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_full_analysis.rst.txt @@ -691,7 +691,7 @@ We make a simple bar plot to summarize the results .. rst-class:: sphx-glr-timing - **Total running time of the script:** (3 minutes 2.248 seconds) + **Total running time of the script:** (2 minutes 44.409 seconds) **Estimated memory usage:** 1355 MB diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_glm_decoding.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_glm_decoding.rst.txt index 29db35e7035..5488ece3a0f 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_glm_decoding.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_glm_decoding.rst.txt @@ -198,7 +198,7 @@ Run the glm on data from each session ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -212,7 +212,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.2s, 0.0min + __________________________________________________filter_and_mask - 1.4s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-0.114769, ..., -2.149296], @@ -221,42 +221,42 @@ Run the glm on data from each session array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.2s, 0.0min + __________________________________________________________run_glm - 1.3s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.44475 , ..., 0.379275]), ) + unmask(array([-1.44475 , ..., 0.379275]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.013524, ..., 0.844135]), ) + unmask(array([-0.013524, ..., 0.844135]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.217486, ..., -1.430348]), ) + unmask(array([ 0.217486, ..., -1.430348]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.106474, ..., -0.182434]), ) + unmask(array([-1.106474, ..., -0.182434]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-2.747494, ..., -1.660679]), ) + unmask(array([-2.747494, ..., -1.660679]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.080159, ..., -1.32614 ]), ) + unmask(array([ 0.080159, ..., -1.32614 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.253894, ..., -0.452682]), ) + unmask(array([-0.253894, ..., -0.452682]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([1.240914, ..., 0.244136]), ) + unmask(array([1.240914, ..., 0.244136]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -270,7 +270,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.0s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[ 12.660587, ..., -13.536042], @@ -279,42 +279,42 @@ Run the glm on data from each session array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.871458, ..., -0.990755]), ) + unmask(array([-1.871458, ..., -0.990755]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.627194, ..., -1.290147]), ) + unmask(array([ 0.627194, ..., -1.290147]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.153013, ..., -1.320123]), ) + unmask(array([-1.153013, ..., -1.320123]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-2.15748 , ..., 2.082416]), ) + unmask(array([-2.15748 , ..., 2.082416]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.997775, ..., -0.754066]), ) + unmask(array([-0.997775, ..., -0.754066]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.109412, ..., 1.330079]), ) + unmask(array([0.109412, ..., 1.330079]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 1.030863, ..., -1.731439]), ) + unmask(array([ 1.030863, ..., -1.731439]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.559734, ..., -0.720924]), ) + unmask(array([-0.559734, ..., -0.720924]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -328,7 +328,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.1s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[ 5.205584, ..., 26.587189], @@ -337,42 +337,42 @@ Run the glm on data from each session array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 1.695564, ..., -0.455092]), ) + unmask(array([ 1.695564, ..., -0.455092]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([1.457214, ..., 1.537178]), ) + unmask(array([1.457214, ..., 1.537178]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.803925, ..., 0.570463]), ) + unmask(array([-0.803925, ..., 0.570463]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-2.614932, ..., 0.232909]), ) + unmask(array([-2.614932, ..., 0.232909]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.527175, ..., -1.062723]), ) + unmask(array([-1.527175, ..., -1.062723]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-2.126756, ..., -2.274819]), ) + unmask(array([-2.126756, ..., -2.274819]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.635166, ..., -0.395548]), ) + unmask(array([ 0.635166, ..., -0.395548]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.250365, ..., 0.364311]), ) + unmask(array([0.250365, ..., 0.364311]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -386,7 +386,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.1s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-2.026206, ..., 5.974948], @@ -395,42 +395,42 @@ Run the glm on data from each session array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.503142, ..., -1.639351]), ) + unmask(array([ 0.503142, ..., -1.639351]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.271132, ..., -0.047089]), ) + unmask(array([-0.271132, ..., -0.047089]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.318104, ..., 0.724813]), ) + unmask(array([0.318104, ..., 0.724813]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.073279, ..., -0.316956]), ) + unmask(array([-0.073279, ..., -0.316956]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 1.380183, ..., -0.690685]), ) + unmask(array([ 1.380183, ..., -0.690685]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.631912, ..., -0.753286]), ) + unmask(array([-1.631912, ..., -0.753286]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.155784, ..., -0.065658]), ) + unmask(array([-1.155784, ..., -0.065658]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.186135, ..., -0.69267 ]), ) + unmask(array([ 0.186135, ..., -0.69267 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -444,7 +444,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.0s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[ 53.033577, ..., -55.45955 ], @@ -453,42 +453,42 @@ Run the glm on data from each session array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.158342, ..., -0.068131]), ) + unmask(array([ 0.158342, ..., -0.068131]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.396497, ..., -0.424937]), ) + unmask(array([ 0.396497, ..., -0.424937]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.091867, ..., 0.463109]), ) + unmask(array([-0.091867, ..., 0.463109]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 1.054041, ..., -0.122921]), ) + unmask(array([ 1.054041, ..., -0.122921]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.025223, ..., 0.562991]), ) + unmask(array([-0.025223, ..., 0.562991]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.001653, ..., -0.968729]), ) + unmask(array([-0.001653, ..., -0.968729]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.274665, ..., -0.667 ]), ) + unmask(array([ 0.274665, ..., -0.667 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.564329, ..., 1.496068]), ) + unmask(array([0.564329, ..., 1.496068]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -502,7 +502,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.0s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-27.150482, ..., -5.81308 ], @@ -511,42 +511,42 @@ Run the glm on data from each session array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.987059, ..., 1.41717 ]), ) + unmask(array([-0.987059, ..., 1.41717 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-2.24774 , ..., 0.674399]), ) + unmask(array([-2.24774 , ..., 0.674399]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.731234, ..., 1.341998]), ) + unmask(array([-0.731234, ..., 1.341998]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.714869, ..., 1.182988]), ) + unmask(array([-0.714869, ..., 1.182988]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.222674, ..., 0.480354]), ) + unmask(array([-1.222674, ..., 0.480354]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.366899, ..., -0.091153]), ) + unmask(array([-1.366899, ..., -0.091153]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.2708 , ..., -0.247146]), ) + unmask(array([-1.2708 , ..., -0.247146]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.251249, ..., -0.413063]), ) - ___________________________________________________________unmask - 0.1s, 0.0min + unmask(array([-1.251249, ..., -0.413063]), ) + ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -560,7 +560,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.1s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[129.51173 , ..., -15.279282], @@ -569,42 +569,42 @@ Run the glm on data from each session array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.290412, ..., -0.609221]), ) + unmask(array([-1.290412, ..., -0.609221]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.090297, ..., -0.822602]), ) + unmask(array([ 0.090297, ..., -0.822602]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 1.747918, ..., -0.108861]), ) + unmask(array([ 1.747918, ..., -0.108861]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 1.095788, ..., -1.376995]), ) + unmask(array([ 1.095788, ..., -1.376995]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.807425, ..., 1.826947]), ) + unmask(array([-0.807425, ..., 1.826947]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.017351, ..., 0.622242]), ) + unmask(array([0.017351, ..., 0.622242]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.625042, ..., -0.231224]), ) + unmask(array([-0.625042, ..., -0.231224]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.056424, ..., -1.672737]), ) + unmask(array([ 0.056424, ..., -1.672737]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -618,7 +618,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.1s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-15.915996, ..., 22.07737 ], @@ -627,42 +627,42 @@ Run the glm on data from each session array([[ 0. , ..., 1. ], ..., [-0.200737, ..., 1. ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.008536, ..., -0.066075]), ) + unmask(array([-0.008536, ..., -0.066075]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 1.168487, ..., -0.636238]), ) + unmask(array([ 1.168487, ..., -0.636238]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([1.145684, ..., 0.932773]), ) + unmask(array([1.145684, ..., 0.932773]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.580823, ..., -0.455655]), ) + unmask(array([ 0.580823, ..., -0.455655]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.685537, ..., 0.715791]), ) + unmask(array([-0.685537, ..., 0.715791]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.245273, ..., -0.099707]), ) + unmask(array([-0.245273, ..., -0.099707]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([1.79538 , ..., 1.913842]), ) + unmask(array([1.79538 , ..., 1.913842]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([3.519925, ..., 0.629218]), ) + unmask(array([3.519925, ..., 0.629218]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -676,7 +676,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.0s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-0.292987, ..., 18.392956], @@ -685,42 +685,42 @@ Run the glm on data from each session array([[ 0. , ..., 1. ], ..., [-0.200737, ..., 1. ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.425611, ..., 2.348025]), ) + unmask(array([-1.425611, ..., 2.348025]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.31867 , ..., 0.408223]), ) + unmask(array([0.31867 , ..., 0.408223]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.534932, ..., -0.150519]), ) + unmask(array([-0.534932, ..., -0.150519]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.149007, ..., 0.640215]), ) + unmask(array([0.149007, ..., 0.640215]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.640699, ..., 1.50369 ]), ) + unmask(array([0.640699, ..., 1.50369 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.246384, ..., -1.346316]), ) + unmask(array([-0.246384, ..., -1.346316]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.162243, ..., -0.519251]), ) + unmask(array([-0.162243, ..., -0.519251]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.203695, ..., -1.335337]), ) + unmask(array([ 0.203695, ..., -1.335337]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -734,7 +734,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.0s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-5.223948, ..., -5.959582], @@ -743,42 +743,42 @@ Run the glm on data from each session array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.034515, ..., 1.612397]), ) + unmask(array([-0.034515, ..., 1.612397]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([1.62798 , ..., 1.160445]), ) + unmask(array([1.62798 , ..., 1.160445]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([1.506632, ..., 0.459388]), ) + unmask(array([1.506632, ..., 0.459388]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.261016, ..., -0.747236]), ) + unmask(array([ 0.261016, ..., -0.747236]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.231796, ..., 1.098904]), ) + unmask(array([-0.231796, ..., 1.098904]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-2.148582, ..., 0.999934]), ) + unmask(array([-2.148582, ..., 0.999934]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-2.548262, ..., 0.09934 ]), ) + unmask(array([-2.548262, ..., 0.09934 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.222824, ..., 0.318977]), ) + unmask(array([-1.222824, ..., 0.318977]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -792,7 +792,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.0s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-19.66533 , ..., -6.299562], @@ -804,39 +804,39 @@ Run the glm on data from each session __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.175763, ..., -3.429485]), ) + unmask(array([-0.175763, ..., -3.429485]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-3.146358, ..., -2.947626]), ) + unmask(array([-3.146358, ..., -2.947626]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.806852, ..., -2.720554]), ) + unmask(array([-1.806852, ..., -2.720554]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.008926, ..., 0.4544 ]), ) + unmask(array([-0.008926, ..., 0.4544 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([1.279543, ..., 1.828183]), ) + unmask(array([1.279543, ..., 1.828183]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.463642, ..., 0.26599 ]), ) + unmask(array([-0.463642, ..., 0.26599 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.058735, ..., -1.191442]), ) + unmask(array([-1.058735, ..., -1.191442]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.390268, ..., -1.112207]), ) + unmask(array([ 0.390268, ..., -1.112207]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask... - _filter_and_mask(, , { 'clean_kwargs': {}, + _filter_and_mask(, , { 'clean_kwargs': {}, 'detrend': False, 'dtype': None, 'high_pass': None, @@ -850,7 +850,7 @@ Run the glm on data from each session 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) - __________________________________________________filter_and_mask - 1.0s, 0.0min + __________________________________________________filter_and_mask - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-1.095605, ..., 16.449202], @@ -859,38 +859,38 @@ Run the glm on data from each session array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None) - __________________________________________________________run_glm - 1.0s, 0.0min + __________________________________________________________run_glm - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.340751, ..., -1.056108]), ) + unmask(array([ 0.340751, ..., -1.056108]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.043261, ..., 1.144442]), ) + unmask(array([0.043261, ..., 1.144442]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([1.517954, ..., 0.611394]), ) + unmask(array([1.517954, ..., 0.611394]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-5.797134e-01, ..., 4.317655e-06]), ) + unmask(array([-5.797134e-01, ..., 4.317655e-06]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([ 0.398581, ..., -0.488427]), ) + unmask(array([ 0.398581, ..., -0.488427]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([0.714396, ..., 0.869941]), ) + unmask(array([0.714396, ..., 0.869941]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-0.924894, ..., 0.723724]), ) + unmask(array([-0.924894, ..., 0.723724]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... - unmask(array([-1.145297, ..., -0.821272]), ) + unmask(array([-1.145297, ..., -0.821272]), ) ___________________________________________________________unmask - 0.1s, 0.0min @@ -1059,32 +1059,32 @@ and have the contrast, we can quickly create a summary report.

Design Matrix:

- Plot of Design Matrix used in Session 1. + Plot of Design Matrix used in Session 1.

Contrasts

- Plot of the contrast: bottle. - Plot of the contrast: house. - Plot of the contrast: chair. - Plot of the contrast: scrambledpix. - Plot of the contrast: face. - Plot of the contrast: shoe. - Plot of the contrast: cat. - Plot of the contrast: scissors. + Plot of the contrast: bottle. + Plot of the contrast: house. + Plot of the contrast: chair. + Plot of the contrast: scrambledpix. + Plot of the contrast: face. + Plot of the contrast: shoe. + Plot of the contrast: cat. + Plot of the contrast: scissors.

Mask

- Model did not supply a mask image.

Stat Maps with Cluster Tables

bottle

- Stat map plot for the contrast: bottle + Stat map plot for the contrast: bottle
Contrast Plot - Plot of the contrast: bottle. + Plot of the contrast: bottle.

@@ -7288,10 +7288,10 @@ and have the contrast, we can quickly create a summary report.

house

- Stat map plot for the contrast: house + Stat map plot for the contrast: house
Contrast Plot - Plot of the contrast: house. + Plot of the contrast: house.

@@ -8903,10 +8903,10 @@ and have the contrast, we can quickly create a summary report.

chair

- Stat map plot for the contrast: chair + Stat map plot for the contrast: chair
Contrast Plot - Plot of the contrast: chair. + Plot of the contrast: chair.

@@ -9390,10 +9390,10 @@ and have the contrast, we can quickly create a summary report.

scrambledpix

- Stat map plot for the contrast: scrambledpix + Stat map plot for the contrast: scrambledpix
Contrast Plot - Plot of the contrast: scrambledpix. + Plot of the contrast: scrambledpix.

@@ -9693,10 +9693,10 @@ and have the contrast, we can quickly create a summary report.

face

- Stat map plot for the contrast: face + Stat map plot for the contrast: face
Contrast Plot - Plot of the contrast: face. + Plot of the contrast: face.

@@ -10660,10 +10660,10 @@ and have the contrast, we can quickly create a summary report.

shoe

- Stat map plot for the contrast: shoe + Stat map plot for the contrast: shoe
Contrast Plot - Plot of the contrast: shoe. + Plot of the contrast: shoe.

@@ -10987,10 +10987,10 @@ and have the contrast, we can quickly create a summary report.

cat

- Stat map plot for the contrast: cat + Stat map plot for the contrast: cat
Contrast Plot - Plot of the contrast: cat. + Plot of the contrast: cat.

@@ -11626,10 +11626,10 @@ and have the contrast, we can quickly create a summary report.

scissors

- Stat map plot for the contrast: scissors + Stat map plot for the contrast: scissors
Contrast Plot - Plot of the contrast: scissors. + Plot of the contrast: scissors.

@@ -13671,7 +13671,7 @@ corresponding conditions labels and session labels .. rst-class:: sphx-glr-timing - **Total running time of the script:** (2 minutes 31.885 seconds) + **Total running time of the script:** (2 minutes 43.285 seconds) **Estimated memory usage:** 917 MB diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_grid_search.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_grid_search.rst.txt index 4fa880cf96c..eae63b12f83 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_grid_search.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_grid_search.rst.txt @@ -199,10 +199,10 @@ excellent explanation of how cross-validation works. The provided image has no sform in its header. Please check the provided file. Results may not be as expected. - Fold 1 | Best SVM parameters: C=1000, penalty=l2, dual=True with score: 0.9008264462809916 + Fold 1 | Best SVM parameters: C=100, penalty=l1, dual=False with score: 0.9380165289256197 Fold 2 | Best SVM parameters: C=1000, penalty=l2, dual=True with score: 0.9177489177489176 - Fold 3 | Best SVM parameters: C=1000, penalty=l1, dual=False with score: 0.7554112554112554 - Fold 4 | Best SVM parameters: C=100, penalty=l1, dual=False with score: 0.8073593073593074 + Fold 3 | Best SVM parameters: C=1000, penalty=l1, dual=False with score: 0.7510822510822511 + Fold 4 | Best SVM parameters: C=1000, penalty=l1, dual=False with score: 0.8484848484848484 Fold 5 | Best SVM parameters: C=1000, penalty=l1, dual=False with score: 0.7554112554112554 @@ -255,8 +255,8 @@ Compute prediction scores with different values of screening percentile The provided image has no sform in its header. Please check the provided file. Results may not be as expected. Sreening Percentile: 2.000 - Mean CV score: 0.8226 - Validation score: 0.5000 + Mean CV score: 0.8367 + Validation score: 0.4444 /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. @@ -269,29 +269,29 @@ Compute prediction scores with different values of screening percentile The provided image has no sform in its header. Please check the provided file. Results may not be as expected. Sreening Percentile: 8.000 - Mean CV score: 0.9067 - Validation score: 0.7222 + Mean CV score: 0.8570 + Validation score: 0.3889 /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. Sreening Percentile: 16.000 - Mean CV score: 0.8630 - Validation score: 0.5000 + Mean CV score: 0.8459 + Validation score: 0.4444 /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. Sreening Percentile: 32.000 - Mean CV score: 0.8270 - Validation score: 0.5556 + Mean CV score: 0.8652 + Validation score: 0.5000 /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. Sreening Percentile: 64.000 - Mean CV score: 0.8722 - Validation score: 0.5556 + Mean CV score: 0.8937 + Validation score: 0.2778 @@ -355,10 +355,6 @@ pipeline. Liblinear failed to converge, increase the number of iterations. - /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: - - Liblinear failed to converge, increase the number of iterations. - /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. @@ -383,10 +379,6 @@ pipeline. Liblinear failed to converge, increase the number of iterations. - /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: - - Liblinear failed to converge, increase the number of iterations. - /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. @@ -399,10 +391,6 @@ pipeline. Liblinear failed to converge, increase the number of iterations. - /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: - - Liblinear failed to converge, increase the number of iterations. - /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. @@ -411,6 +399,10 @@ pipeline. Liblinear failed to converge, increase the number of iterations. + /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: + + Liblinear failed to converge, increase the number of iterations. + /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. @@ -455,11 +447,15 @@ pipeline. The provided image has no sform in its header. Please check the provided file. Results may not be as expected. + /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: + + Liblinear failed to converge, increase the number of iterations. + /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected. - Nested CV score: 0.6528 + Nested CV score: 0.6389 @@ -508,7 +504,7 @@ Plot the prediction scores using matplotlib .. rst-class:: sphx-glr-timing - **Total running time of the script:** (2 minutes 52.295 seconds) + **Total running time of the script:** (3 minutes 19.153 seconds) **Estimated memory usage:** 916 MB diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_multiclass.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_multiclass.rst.txt index 576a568f4a7..701bf9ad73b 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_multiclass.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_multiclass.rst.txt @@ -293,9 +293,9 @@ last 2 sessions .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 51.611 seconds) + **Total running time of the script:** (0 minutes 55.472 seconds) -**Estimated memory usage:** 3114 MB +**Estimated memory usage:** 3125 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_multiclass.py: diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_searchlight.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_searchlight.rst.txt index f96ee6b54e0..448f564cdf1 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_searchlight.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_searchlight.rst.txt @@ -180,17 +180,17 @@ Searchlight computation The provided image has no sform in its header. Please check the provided file. Results may not be as expected. - Job #1, processed 0/739 voxels (0.00%, 423.5529899597168 seconds remaining) Job #1, processed 10/739 voxels (1.35%, 34.42149208210133 seconds remaining) Job #1, processed 20/739 voxels (2.71%, 34.54305851415515 seconds remaining) Job #1, processed 30/739 voxels (4.06%, 35.157494893802216 seconds remaining) Job #1, processed 40/739 voxels (5.41%, 34.86122745859424 seconds remaining) Job #1, processed 50/739 voxels (6.77%, 34.661052783799846 seconds remaining) Job #1, processed 60/739 voxels (8.12%, 34.372675688983186 seconds remaining) Job #1, processed 70/739 voxels (9.47%, 34.08667111094675 seconds remaining) Job #1, processed 80/739 voxels (10.83%, 33.7684923622857 seconds remaining) Job #1, processed 90/739 voxels (12.18%, 33.379006258568346 seconds remaining) Job #1, processed 100/739 voxels (13.53%, 33.03083991986773 seconds remaining) Job #1, processed 110/739 voxels (14.88%, 32.586199975782826 seconds remaining) Job #1, processed 120/739 voxels 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SearchLight(cv=KFold(n_splits=4, random_state=None, shuffle=False),
-                mask_img=<nibabel.nifti1.Nifti1Image object at 0x7fc8fd8325e0>,
-                process_mask_img=<nibabel.nifti1.Nifti1Image object at 0x7fc910efb280>,
+                mask_img=<nibabel.nifti1.Nifti1Image object at 0x7fac7bb6b100>,
+                process_mask_img=<nibabel.nifti1.Nifti1Image object at 0x7fac7bee37f0>,
                 radius=5.6, verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

@@ -304,9 +304,9 @@ Use the fmri mean image as a surrogate of anatomical data .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 49.138 seconds) + **Total running time of the script:** (0 minutes 52.164 seconds) -**Estimated memory usage:** 917 MB +**Estimated memory usage:** 916 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_searchlight.py: diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_searchlight_surface.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_searchlight_surface.rst.txt index 0fac3b4fdbf..d50634ea229 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_searchlight_surface.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_searchlight_surface.rst.txt @@ -186,9 +186,9 @@ Visualization .. rst-class:: sphx-glr-timing - **Total running time of the script:** (2 minutes 0.175 seconds) + **Total running time of the script:** (2 minutes 8.983 seconds) -**Estimated memory usage:** 916 MB +**Estimated memory usage:** 917 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_searchlight_surface.py: diff --git a/dev/_sources/auto_examples/02_decoding/plot_haxby_stimuli.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_haxby_stimuli.rst.txt index 7e76487a0bd..b766384f10c 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_haxby_stimuli.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_haxby_stimuli.rst.txt @@ -127,9 +127,9 @@ Cortex" (Science 2001) .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 6.964 seconds) + **Total running time of the script:** (0 minutes 7.266 seconds) -**Estimated memory usage:** 46 MB +**Estimated memory usage:** 44 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_stimuli.py: diff --git a/dev/_sources/auto_examples/02_decoding/plot_mixed_gambles_frem.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_mixed_gambles_frem.rst.txt index 64a3e51a4a9..1e3a578c575 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_mixed_gambles_frem.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_mixed_gambles_frem.rst.txt @@ -223,7 +223,7 @@ We compare both of these models to a pipeline ensembling many models Solver terminated early (max_iter=10000). Consider pre-processing your data with StandardScaler or MinMaxScaler. - + @@ -279,9 +279,9 @@ few minutes. Example code is included below. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 52.765 seconds) + **Total running time of the script:** (0 minutes 54.854 seconds) -**Estimated memory usage:** 1831 MB +**Estimated memory usage:** 1835 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_mixed_gambles_frem.py: diff --git a/dev/_sources/auto_examples/02_decoding/plot_miyawaki_encoding.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_miyawaki_encoding.rst.txt index 95b6cd8950c..6465fd32033 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_miyawaki_encoding.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_miyawaki_encoding.rst.txt @@ -496,7 +496,7 @@ set of regression coefficients. .. code-block:: none - + @@ -511,7 +511,7 @@ its location in the brain. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 22.236 seconds) + **Total running time of the script:** (0 minutes 26.682 seconds) **Estimated memory usage:** 417 MB diff --git a/dev/_sources/auto_examples/02_decoding/plot_miyawaki_reconstruction.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_miyawaki_reconstruction.rst.txt index 08b264645c8..7ceec923636 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_miyawaki_reconstruction.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_miyawaki_reconstruction.rst.txt @@ -93,7 +93,7 @@ First we load the Miyawaki dataset .. code-block:: none Fetching dataset...First functional nifti image (4D) is located at: /home/runner/work/nilearn/nilearn/nilearn_data/miyawaki2008/func/data_figure_run01.nii.gz - Done (0.15s). + Done (0.20s). @@ -200,7 +200,7 @@ Then we prepare and mask the data .. code-block:: none - Preprocessing data... Done (25.66s). + Preprocessing data... Done (29.78s). @@ -253,7 +253,7 @@ We define our prediction function .. code-block:: none - Training classifiers... Training classifiers 001/361... Training classifiers 002/361... Training classifiers 003/361... Training classifiers 004/361... Training classifiers 005/361... Training classifiers 006/361... Training classifiers 007/361... Training classifiers 008/361... Training classifiers 009/361... Training classifiers 010/361... Training classifiers 011/361... Training classifiers 012/361... Training classifiers 013/361... Training classifiers 014/361... Training classifiers 015/361... Training classifiers 016/361... Training classifiers 017/361... Training classifiers 018/361... Training classifiers 019/361... Training classifiers 020/361... Training classifiers 021/361... Training classifiers 022/361... Training classifiers 023/361... Training classifiers 024/361... Training classifiers 025/361... Training classifiers 026/361... 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Done (3.22s). @@ -520,7 +520,7 @@ ground truth .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 5.755 seconds) + **Total running time of the script:** (1 minutes 19.917 seconds) **Estimated memory usage:** 417 MB diff --git a/dev/_sources/auto_examples/02_decoding/plot_oasis_vbm.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_oasis_vbm.rst.txt index 00723c22f3b..6e49ecf74f0 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_oasis_vbm.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_oasis_vbm.rst.txt @@ -310,7 +310,7 @@ Visualize the quality of predictions .. code-block:: none - + @@ -382,8 +382,8 @@ Inference with massively univariate model .. code-block:: none Massively univariate model - Job #1, processed 0/2000 permutations (0.00%, 193.35508346557617 seconds remaining) Job #1, processed 10/2000 permutations (0.50%, 33.88106632232666 seconds remaining) Job #1, processed 20/2000 permutations (1.00%, 32.99982690811157 seconds remaining) Job #1, processed 30/2000 permutations (1.50%, 31.650777101516727 seconds remaining) Job #1, processed 40/2000 permutations (2.00%, 30.821441411972046 seconds remaining) Job #1, processed 50/2000 permutations (2.50%, 30.287153720855713 seconds remaining) Job #1, processed 60/2000 permutations (3.00%, 29.87761966387431 seconds remaining) Job #1, processed 70/2000 permutations (3.50%, 29.612723350524902 seconds remaining) Job #1, processed 80/2000 permutations (4.00%, 29.344837188720703 seconds remaining) Job #1, processed 90/2000 permutations (4.50%, 29.459222502178616 seconds remaining) Job #1, processed 100/2000 permutations (5.00%, 29.228153705596924 seconds remaining) Job #1, processed 110/2000 permutations (5.50%, 28.98711250045083 seconds remaining) Job #1, processed 120/2000 permutations (6.00%, 28.789533456166584 seconds remaining) Job #1, processed 130/2000 permutations (6.50%, 28.59211802482605 seconds remaining) Job #1, processed 140/2000 permutations (7.00%, 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processed 720/2000 permutations (36.00%, 19.825477176242405 seconds remaining) Job #1, processed 730/2000 permutations (36.50%, 19.660248753142685 seconds remaining) Job #1, processed 740/2000 permutations (37.00%, 19.49359797787022 seconds remaining) Job #1, processed 750/2000 permutations (37.50%, 19.349790811538696 seconds remaining) Job #1, processed 760/2000 permutations (38.00%, 19.183915652726824 seconds remaining) Job #1, processed 770/2000 permutations (38.50%, 19.019928542050447 seconds remaining) Job #1, processed 780/2000 permutations (39.00%, 18.85541388316032 seconds remaining) Job #1, processed 790/2000 permutations (39.50%, 18.731071879592122 seconds remaining) Job #1, processed 800/2000 permutations (40.00%, 18.56532096862793 seconds remaining) Job #1, processed 810/2000 permutations (40.50%, 18.42011696909681 seconds remaining) Job #1, processed 820/2000 permutations (41.00%, 18.256150094474233 seconds remaining) Job #1, processed 830/2000 permutations (41.50%, 18.092118381017663 seconds remaining) Job #1, processed 840/2000 permutations (42.00%, 17.927541085651942 seconds remaining) Job #1, processed 850/2000 permutations (42.50%, 17.765961913501517 seconds remaining) Job #1, processed 860/2000 permutations (43.00%, 17.604031878848406 seconds remaining) Job #1, processed 870/2000 permutations (43.50%, 17.440619581047148 seconds remaining) Job #1, processed 880/2000 permutations (44.00%, 17.295843427831475 seconds remaining) Job #1, processed 890/2000 permutations (44.50%, 17.134189817342865 seconds remaining) Job #1, processed 900/2000 permutations (45.00%, 16.974654383129543 seconds remaining) Job #1, processed 910/2000 permutations (45.50%, 16.812963373058444 seconds remaining) Job #1, processed 920/2000 permutations (46.00%, 16.653652823489647 seconds remaining) Job #1, processed 930/2000 permutations (46.50%, 16.49902750343405 seconds remaining) Job #1, processed 940/2000 permutations (47.00%, 16.338829314455072 seconds remaining) Job #1, processed 950/2000 permutations (47.50%, 16.19455764168187 seconds remaining) Job #1, processed 960/2000 permutations (48.00%, 16.033485929171242 seconds remaining) Job #1, processed 970/2000 permutations (48.50%, 15.874664343509478 seconds remaining) Job #1, processed 980/2000 permutations (49.00%, 15.715232338224139 seconds remaining) Job #1, processed 990/2000 permutations (49.50%, 15.555402546217946 seconds remaining) Job #1, processed 1000/2000 permutations (50.00%, 15.395453453063965 seconds remaining) Job #1, processed 1010/2000 permutations (50.50%, 15.347075906130346 seconds remaining) Job #1, processed 1020/2000 permutations (51.00%, 15.187024340910071 seconds remaining) Job #1, processed 1030/2000 permutations (51.50%, 15.025456715556025 seconds remaining) Job #1, processed 1040/2000 permutations (52.00%, 14.864180638239935 seconds remaining) Job #1, processed 1050/2000 permutations (52.50%, 14.70415194829305 seconds remaining) Job #1, processed 1060/2000 permutations (53.00%, 14.543467773581451 seconds remaining) Job #1, processed 1070/2000 permutations (53.50%, 14.383220218052374 seconds remaining) Job #1, processed 1080/2000 permutations (54.00%, 14.235538562138876 seconds remaining) Job #1, processed 1090/2000 permutations (54.50%, 14.077074129647071 seconds remaining) Job #1, processed 1100/2000 permutations (55.00%, 13.91778243671764 seconds remaining) Job #1, processed 1110/2000 permutations (55.50%, 13.758312996443328 seconds remaining) Job #1, processed 1120/2000 permutations (56.00%, 13.600948776517596 seconds remaining) Job #1, processed 1130/2000 permutations (56.50%, 13.441847961560814 seconds remaining) Job #1, processed 1140/2000 permutations (57.00%, 13.284148262258162 seconds remaining) Job #1, processed 1150/2000 permutations (57.50%, 13.13656997680664 seconds remaining) Job #1, processed 1160/2000 permutations (58.00%, 12.979894588733542 seconds remaining) Job #1, processed 1170/2000 permutations (58.50%, 12.821433809068468 seconds remaining) Job #1, processed 1180/2000 permutations (59.00%, 12.67323608721717 seconds remaining) Job #1, processed 1190/2000 permutations (59.50%, 12.51407267265961 seconds remaining) Job #1, processed 1200/2000 permutations (60.00%, 12.355065504709879 seconds remaining) Job #1, processed 1210/2000 permutations (60.50%, 12.196064700765058 seconds remaining) Job #1, processed 1220/2000 permutations (61.00%, 12.048168874177778 seconds remaining) Job #1, processed 1230/2000 permutations (61.50%, 11.89019058002689 seconds remaining) Job #1, processed 1240/2000 permutations (62.00%, 11.732203022126228 seconds remaining) Job #1, processed 1250/2000 permutations (62.50%, 11.581426048278809 seconds remaining) Job #1, processed 1260/2000 permutations (63.00%, 11.424798261551631 seconds remaining) Job #1, processed 1270/2000 permutations (63.50%, 11.266934475560825 seconds remaining) Job #1, processed 1280/2000 permutations (64.00%, 11.118078231811523 seconds remaining) Job #1, processed 1290/2000 permutations (64.50%, 10.960978419281716 seconds remaining) Job #1, processed 1300/2000 permutations (65.00%, 10.803261793576754 seconds remaining) Job #1, processed 1310/2000 permutations (65.50%, 10.645680542210586 seconds remaining) Job #1, processed 1320/2000 permutations (66.00%, 10.488268751086611 seconds remaining) Job #1, processed 1330/2000 permutations (66.50%, 10.331025148692884 seconds remaining) Job #1, processed 1340/2000 permutations (67.00%, 10.174037175392037 seconds remaining) Job #1, processed 1350/2000 permutations (67.50%, 10.024100603880704 seconds remaining) Job #1, processed 1360/2000 permutations (68.00%, 9.866543938131894 seconds remaining) Job #1, processed 1370/2000 permutations (68.50%, 9.709839669457317 seconds remaining) Job #1, processed 1380/2000 permutations (69.00%, 9.554099525230518 seconds remaining) Job #1, processed 1390/2000 permutations (69.50%, 9.396998853134594 seconds remaining) Job #1, processed 1400/2000 permutations (70.00%, 9.240907226290021 seconds remaining) Job #1, processed 1410/2000 permutations (70.50%, 9.120296295652999 seconds remaining) Job #1, processed 1420/2000 permutations (71.00%, 8.96954661020091 seconds remaining) Job #1, processed 1430/2000 permutations (71.50%, 8.812601806400538 seconds remaining) Job #1, processed 1440/2000 permutations (72.00%, 8.655020078023275 seconds remaining) Job #1, processed 1450/2000 permutations (72.50%, 8.498025030925355 seconds remaining) Job #1, processed 1460/2000 permutations (73.00%, 8.341322039904659 seconds remaining) Job #1, processed 1470/2000 permutations (73.50%, 8.184370606934943 seconds remaining) Job #1, processed 1480/2000 permutations (74.00%, 8.033222939517048 seconds remaining) Job #1, processed 1490/2000 permutations (74.50%, 7.877526576086979 seconds remaining) Job #1, processed 1500/2000 permutations (75.00%, 7.721157232920328 seconds remaining) Job #1, processed 1510/2000 permutations (75.50%, 7.564404269717387 seconds remaining) Job #1, processed 1520/2000 permutations (76.00%, 7.4079042735852685 seconds remaining) Job #1, processed 1530/2000 permutations (76.50%, 7.251990257524977 seconds remaining) Job #1, processed 1540/2000 permutations (77.00%, 7.096338755124575 seconds remaining) Job #1, processed 1550/2000 permutations (77.50%, 6.945256925398304 seconds remaining) Job #1, processed 1560/2000 permutations (78.00%, 6.7889189292223024 seconds remaining) Job #1, processed 1570/2000 permutations (78.50%, 6.633926332376565 seconds remaining) Job #1, processed 1580/2000 permutations (79.00%, 6.479456491108182 seconds remaining) Job #1, processed 1590/2000 permutations (79.50%, 6.323790116879925 seconds remaining) Job #1, processed 1600/2000 permutations (80.00%, 6.1681236028671265 seconds remaining) Job #1, processed 1610/2000 permutations (80.50%, 6.013084955096986 seconds remaining) Job #1, processed 1620/2000 permutations (81.00%, 5.861154129475723 seconds remaining) Job #1, processed 1630/2000 permutations (81.50%, 5.705546159685755 seconds remaining) Job #1, processed 1640/2000 permutations (82.00%, 5.550072216406101 seconds remaining) Job #1, processed 1650/2000 permutations (82.50%, 5.394633018609249 seconds remaining) Job #1, processed 1660/2000 permutations (83.00%, 5.2393038531383835 seconds remaining) Job #1, processed 1670/2000 permutations (83.50%, 5.084007127556259 seconds remaining) Job #1, processed 1680/2000 permutations (84.00%, 4.932221548897879 seconds remaining) Job #1, processed 1690/2000 permutations (84.50%, 4.777759911745963 seconds remaining) Job #1, processed 1700/2000 permutations (85.00%, 4.622888004078585 seconds remaining) Job #1, processed 1710/2000 permutations (85.50%, 4.468025093190154 seconds remaining) Job #1, processed 1720/2000 permutations (86.00%, 4.312921313352363 seconds remaining) Job #1, processed 1730/2000 permutations (86.50%, 4.157820307450487 seconds remaining) Job #1, processed 1740/2000 permutations (87.00%, 4.003143094051843 seconds remaining) Job #1, processed 1750/2000 permutations (87.50%, 3.850528342383248 seconds remaining) Job #1, processed 1760/2000 permutations (88.00%, 3.6962542858990752 seconds remaining) Job #1, processed 1770/2000 permutations (88.50%, 3.5451416403560314 seconds remaining) Job #1, processed 1780/2000 permutations (89.00%, 3.3902285875899065 seconds remaining) Job #1, processed 1790/2000 permutations (89.50%, 3.2353703203148014 seconds remaining) Job #1, processed 1800/2000 permutations (90.00%, 3.080731497870551 seconds remaining) Job #1, processed 1810/2000 permutations (90.50%, 2.9316416761493156 seconds remaining) Job #1, processed 1820/2000 permutations (91.00%, 2.780884855396145 seconds remaining) Job #1, processed 1830/2000 permutations (91.50%, 2.6263920466105146 seconds remaining) Job #1, processed 1840/2000 permutations (92.00%, 2.4714540813280186 seconds remaining) Job #1, processed 1850/2000 permutations (92.50%, 2.316464153495995 seconds remaining) Job #1, processed 1860/2000 permutations (93.00%, 2.161579506371611 seconds remaining) Job #1, processed 1870/2000 permutations (93.50%, 2.0068136880742036 seconds remaining) Job #1, processed 1880/2000 permutations (94.00%, 1.8531722667369435 seconds remaining) Job #1, processed 1890/2000 permutations (94.50%, 1.6983817304883684 seconds remaining) Job #1, processed 1900/2000 permutations (95.00%, 1.5437412387446352 seconds remaining) Job #1, processed 1910/2000 permutations (95.50%, 1.389124564475414 seconds remaining) Job #1, processed 1920/2000 permutations (96.00%, 1.2345227599143982 seconds remaining) Job #1, processed 1930/2000 permutations (96.50%, 1.0800421003232965 seconds remaining) Job #1, processed 1940/2000 permutations (97.00%, 0.9255976357410863 seconds remaining) Job #1, processed 1950/2000 permutations (97.50%, 0.7715532657427665 seconds remaining) Job #1, processed 1960/2000 permutations (98.00%, 0.6171818558050661 seconds remaining) Job #1, processed 1970/2000 permutations (98.50%, 0.4627971697579786 seconds remaining) Job #1, processed 1980/2000 permutations (99.00%, 0.3084801808752195 seconds remaining) Job #1, processed 1990/2000 permutations (99.50%, 0.1542104141197013 seconds remaining) - 1933 detections + Job #1, processed 0/2000 permutations (0.00%, 199.17726516723633 seconds remaining) Job #1, processed 10/2000 permutations (0.50%, 35.513943672180176 seconds remaining) Job #1, processed 20/2000 permutations (1.00%, 33.1911563873291 seconds remaining) Job #1, processed 30/2000 permutations (1.50%, 32.351922273635864 seconds remaining) Job #1, processed 40/2000 permutations (2.00%, 32.24910259246826 seconds remaining) Job #1, processed 50/2000 permutations (2.50%, 32.052040815353394 seconds remaining) Job #1, processed 60/2000 permutations (3.00%, 31.654243787129722 seconds remaining) Job #1, processed 70/2000 permutations (3.50%, 31.419612237385344 seconds remaining) Job #1, processed 80/2000 permutations (4.00%, 31.108074188232422 seconds remaining) Job #1, processed 90/2000 permutations (4.50%, 30.792543278800114 seconds remaining) Job #1, processed 100/2000 permutations (5.00%, 30.495018482208252 seconds remaining) Job #1, processed 110/2000 permutations (5.50%, 30.606887405568905 seconds remaining) Job #1, processed 120/2000 permutations (6.00%, 30.344160000483193 seconds remaining) Job #1, processed 130/2000 permutations (6.50%, 30.110843603427593 seconds remaining) Job #1, processed 140/2000 permutations (7.00%, 29.908904007502965 seconds remaining) Job #1, processed 150/2000 permutations (7.50%, 29.691253582636516 seconds remaining) Job #1, processed 160/2000 permutations (8.00%, 29.484394788742065 seconds remaining) Job #1, processed 170/2000 permutations (8.50%, 30.333533006555896 seconds remaining) Job #1, processed 180/2000 permutations (9.00%, 30.065338399675156 seconds remaining) Job #1, processed 190/2000 permutations (9.50%, 29.82857131958008 seconds remaining) Job #1, processed 200/2000 permutations (10.00%, 29.578924655914307 seconds remaining) Job #1, processed 210/2000 permutations (10.50%, 29.34360545022147 seconds remaining) Job #1, processed 220/2000 permutations (11.00%, 29.111905358054425 seconds remaining) Job #1, processed 230/2000 permutations (11.50%, 28.894121709077258 seconds remaining) Job #1, processed 240/2000 permutations (12.00%, 28.848892847696938 seconds remaining) Job #1, processed 250/2000 permutations (12.50%, 28.634715795516968 seconds remaining) Job #1, processed 260/2000 permutations (13.00%, 28.4279826604403 seconds remaining) Job #1, processed 270/2000 permutations (13.50%, 28.241629079536157 seconds remaining) Job #1, processed 280/2000 permutations (14.00%, 28.752962623323715 seconds remaining) Job #1, processed 290/2000 permutations (14.50%, 28.85086391712057 seconds remaining) Job #1, processed 300/2000 permutations (15.00%, 28.672829071680706 seconds remaining) Job #1, processed 310/2000 permutations (15.50%, 28.498755962617935 seconds remaining) Job #1, processed 320/2000 permutations (16.00%, 28.33128386735916 seconds remaining) Job #1, processed 330/2000 permutations (16.50%, 28.17229576544328 seconds remaining) Job #1, processed 340/2000 permutations (17.00%, 28.012633632211127 seconds remaining) Job #1, processed 350/2000 permutations (17.50%, 27.91798394066947 seconds remaining) Job #1, processed 360/2000 permutations (18.00%, 27.74693671862284 seconds remaining) Job #1, processed 370/2000 permutations (18.50%, 27.57120705939628 seconds remaining) Job #1, processed 380/2000 permutations (19.00%, 27.392409437581115 seconds remaining) Job #1, processed 390/2000 permutations (19.50%, 27.223212767870002 seconds remaining) Job #1, processed 400/2000 permutations (20.00%, 27.042736053466797 seconds remaining) Job #1, processed 410/2000 permutations (20.50%, 26.915415496360964 seconds remaining) Job #1, processed 420/2000 permutations (21.00%, 26.75501283009847 seconds remaining) Job #1, processed 430/2000 permutations (21.50%, 26.576377120128896 seconds remaining) Job #1, processed 440/2000 permutations (22.00%, 26.394596099853516 seconds remaining) Job #1, processed 450/2000 permutations (22.50%, 26.215197960535686 seconds remaining) Job #1, processed 460/2000 permutations (23.00%, 26.046176257340807 seconds remaining) Job #1, processed 470/2000 permutations (23.50%, 25.87035509880553 seconds remaining) Job #1, processed 480/2000 permutations (24.00%, 25.76705745855967 seconds remaining) Job #1, processed 490/2000 permutations (24.50%, 25.683832309683975 seconds remaining) Job #1, processed 500/2000 permutations (25.00%, 25.50395894050598 seconds remaining) Job #1, processed 510/2000 permutations (25.50%, 25.31357821296243 seconds remaining) Job #1, processed 520/2000 permutations (26.00%, 25.1761865065648 seconds remaining) Job #1, processed 530/2000 permutations (26.50%, 25.247260093688965 seconds remaining) Job #1, processed 540/2000 permutations (27.00%, 25.036041648299605 seconds remaining) Job #1, processed 550/2000 permutations (27.50%, 24.824515125968237 seconds remaining) Job #1, processed 560/2000 permutations (28.00%, 24.619867801666263 seconds remaining) Job #1, processed 570/2000 permutations (28.50%, 24.418846874906304 seconds remaining) Job #1, processed 580/2000 permutations (29.00%, 24.21589371253704 seconds remaining) Job #1, processed 590/2000 permutations (29.50%, 24.050797583693164 seconds remaining) Job #1, processed 600/2000 permutations (30.00%, 23.873605489730835 seconds remaining) Job #1, processed 610/2000 permutations (30.50%, 23.674583607032652 seconds remaining) Job #1, processed 620/2000 permutations (31.00%, 23.468385619501912 seconds remaining) Job #1, processed 630/2000 permutations (31.50%, 23.265546643544756 seconds remaining) Job #1, processed 640/2000 permutations (32.00%, 23.06389093399048 seconds remaining) Job #1, processed 650/2000 permutations (32.50%, 22.860860604506275 seconds remaining) Job #1, processed 660/2000 permutations (33.00%, 22.708413102410056 seconds remaining) Job #1, processed 670/2000 permutations (33.50%, 22.50513064683373 seconds remaining) Job #1, processed 680/2000 permutations (34.00%, 22.305337639415963 seconds remaining) Job #1, processed 690/2000 permutations (34.50%, 22.108019317405812 seconds remaining) Job #1, processed 700/2000 permutations (35.00%, 21.91792038508824 seconds remaining) Job #1, processed 710/2000 permutations (35.50%, 21.724454953636922 seconds remaining) Job #1, processed 720/2000 permutations (36.00%, 21.536582522922092 seconds remaining) Job #1, processed 730/2000 permutations (36.50%, 21.390912186609558 seconds remaining) Job #1, processed 740/2000 permutations (37.00%, 21.2080786808117 seconds remaining) Job #1, processed 750/2000 permutations (37.50%, 21.015162070592247 seconds remaining) Job #1, processed 760/2000 permutations (38.00%, 20.828021137337934 seconds remaining) Job #1, processed 770/2000 permutations (38.50%, 20.63775729204153 seconds remaining) Job #1, processed 780/2000 permutations (39.00%, 20.449570472423847 seconds remaining) Job #1, processed 790/2000 permutations (39.50%, 20.30224740052525 seconds remaining) Job #1, processed 800/2000 permutations (40.00%, 20.115921020507812 seconds remaining) Job #1, processed 810/2000 permutations (40.50%, 19.92978733262898 seconds remaining) Job #1, processed 820/2000 permutations (41.00%, 19.751883512589988 seconds remaining) Job #1, processed 830/2000 permutations (41.50%, 19.572362138564326 seconds remaining) Job #1, processed 840/2000 permutations (42.00%, 19.39012244769505 seconds remaining) Job #1, processed 850/2000 permutations (42.50%, 19.202644853030936 seconds remaining) Job #1, processed 860/2000 permutations (43.00%, 19.056230622668597 seconds remaining) Job #1, processed 870/2000 permutations (43.50%, 18.876980096444317 seconds remaining) Job #1, processed 880/2000 permutations (44.00%, 18.69908467206088 seconds remaining) Job #1, processed 890/2000 permutations (44.50%, 18.51772068323714 seconds remaining) Job #1, processed 900/2000 permutations (45.00%, 18.341179582807754 seconds remaining) Job #1, processed 910/2000 permutations (45.50%, 18.309931969904635 seconds remaining) Job #1, processed 920/2000 permutations (46.00%, 18.137983467267908 seconds remaining) Job #1, processed 930/2000 permutations (46.50%, 17.963656153730167 seconds remaining) Job #1, processed 940/2000 permutations (47.00%, 17.79442422947985 seconds remaining) Job #1, processed 950/2000 permutations (47.50%, 17.62170918364274 seconds remaining) Job #1, processed 960/2000 permutations (48.00%, 17.450869838396706 seconds remaining) Job #1, processed 970/2000 permutations (48.50%, 17.278935798664683 seconds remaining) Job #1, processed 980/2000 permutations (49.00%, 17.126092103062845 seconds remaining) Job #1, processed 990/2000 permutations (49.50%, 16.958812978532578 seconds remaining) Job #1, processed 1000/2000 permutations (50.00%, 16.784727573394775 seconds remaining) Job #1, processed 1010/2000 permutations (50.50%, 16.60744838667388 seconds remaining) Job #1, processed 1020/2000 permutations (51.00%, 16.429712669522154 seconds remaining) Job #1, processed 1030/2000 permutations (51.50%, 16.2538232710755 seconds remaining) Job #1, processed 1040/2000 permutations (52.00%, 16.099591915424053 seconds remaining) Job #1, processed 1050/2000 permutations (52.50%, 15.924583196640015 seconds remaining) Job #1, processed 1060/2000 permutations (53.00%, 15.751741476778715 seconds remaining) Job #1, processed 1070/2000 permutations (53.50%, 15.577624450220126 seconds remaining) Job #1, processed 1080/2000 permutations (54.00%, 15.40452950089066 seconds remaining) Job #1, processed 1090/2000 permutations (54.50%, 15.238275377028579 seconds remaining) Job #1, processed 1100/2000 permutations (55.00%, 15.074893062764948 seconds remaining) Job #1, processed 1110/2000 permutations (55.50%, 14.898109979457685 seconds remaining) Job #1, processed 1120/2000 permutations (56.00%, 14.723011136054993 seconds remaining) Job #1, processed 1130/2000 permutations (56.50%, 14.54880097060077 seconds remaining) Job #1, processed 1140/2000 permutations (57.00%, 14.373739698476959 seconds remaining) Job #1, processed 1150/2000 permutations (57.50%, 14.19889815993931 seconds remaining) Job #1, processed 1160/2000 permutations (58.00%, 14.026898490971533 seconds remaining) Job #1, processed 1170/2000 permutations (58.50%, 13.86537367462093 seconds remaining) Job #1, processed 1180/2000 permutations (59.00%, 13.696478148638192 seconds remaining) Job #1, processed 1190/2000 permutations (59.50%, 13.524208960412931 seconds remaining) Job #1, processed 1200/2000 permutations (60.00%, 13.349293549855549 seconds remaining) Job #1, processed 1210/2000 permutations (60.50%, 13.17642530725022 seconds remaining) Job #1, processed 1220/2000 permutations (61.00%, 13.002472846234433 seconds remaining) Job #1, processed 1230/2000 permutations (61.50%, 12.837158113960328 seconds remaining) Job #1, processed 1240/2000 permutations (62.00%, 12.665493019165531 seconds remaining) Job #1, processed 1250/2000 permutations (62.50%, 12.495159387588501 seconds remaining) Job #1, processed 1260/2000 permutations (63.00%, 12.321219750813077 seconds remaining) Job #1, processed 1270/2000 permutations (63.50%, 12.149836643474307 seconds remaining) Job #1, processed 1280/2000 permutations (64.00%, 12.030482992529869 seconds remaining) Job #1, processed 1290/2000 permutations (64.50%, 11.870701338893683 seconds remaining) Job #1, processed 1300/2000 permutations (65.00%, 11.697899451622595 seconds remaining) Job #1, processed 1310/2000 permutations (65.50%, 11.526410530541689 seconds remaining) Job #1, processed 1320/2000 permutations (66.00%, 11.355171767148104 seconds remaining) Job #1, processed 1330/2000 permutations (66.50%, 11.183196480112866 seconds remaining) Job #1, processed 1340/2000 permutations (67.00%, 11.026593404029732 seconds remaining) Job #1, processed 1350/2000 permutations (67.50%, 10.859094628581294 seconds remaining) Job #1, processed 1360/2000 permutations (68.00%, 10.690381667193245 seconds remaining) Job #1, processed 1370/2000 permutations (68.50%, 10.51962303593211 seconds remaining) Job #1, processed 1380/2000 permutations (69.00%, 10.346469454143358 seconds remaining) Job #1, processed 1390/2000 permutations (69.50%, 10.174411080724044 seconds remaining) Job #1, processed 1400/2000 permutations (70.00%, 10.015874147415161 seconds remaining) Job #1, processed 1410/2000 permutations (70.50%, 9.843887545538287 seconds remaining) Job #1, processed 1420/2000 permutations (71.00%, 9.683337856346453 seconds remaining) Job #1, processed 1430/2000 permutations (71.50%, 9.512344787170838 seconds remaining) Job #1, processed 1440/2000 permutations (72.00%, 9.341447485817804 seconds remaining) Job #1, processed 1450/2000 permutations (72.50%, 9.172630860887724 seconds remaining) Job #1, processed 1460/2000 permutations (73.00%, 9.004838087787366 seconds remaining) Job #1, processed 1470/2000 permutations (73.50%, 8.83708395925509 seconds remaining) Job #1, processed 1480/2000 permutations (74.00%, 8.675761963870075 seconds remaining) Job #1, processed 1490/2000 permutations (74.50%, 8.507062350343539 seconds remaining) Job #1, processed 1500/2000 permutations (75.00%, 8.337508996327717 seconds remaining) Job #1, processed 1510/2000 permutations (75.50%, 8.16878950516909 seconds remaining) Job #1, processed 1520/2000 permutations (76.00%, 8.000759651786403 seconds remaining) Job #1, processed 1530/2000 permutations (76.50%, 7.833552466498482 seconds remaining) Job #1, processed 1540/2000 permutations (77.00%, 7.669496883045543 seconds remaining) Job #1, processed 1550/2000 permutations (77.50%, 7.502389669418336 seconds remaining) Job #1, processed 1560/2000 permutations (78.00%, 7.333418736091027 seconds remaining) Job #1, processed 1570/2000 permutations (78.50%, 7.165155269537761 seconds remaining) Job #1, processed 1580/2000 permutations (79.00%, 6.996489902085895 seconds remaining) Job #1, processed 1590/2000 permutations (79.50%, 6.82833287700917 seconds remaining) Job #1, processed 1600/2000 permutations (80.00%, 6.6598716378211975 seconds remaining) Job #1, processed 1610/2000 permutations (80.50%, 6.4960789754523995 seconds remaining) Job #1, processed 1620/2000 permutations (81.00%, 6.327970372305976 seconds remaining) Job #1, processed 1630/2000 permutations (81.50%, 6.160059174145657 seconds remaining) Job #1, processed 1640/2000 permutations (82.00%, 5.993317121412696 seconds remaining) Job #1, processed 1650/2000 permutations (82.50%, 5.839470039714467 seconds remaining) Job #1, processed 1660/2000 permutations (83.00%, 5.676504522921091 seconds remaining) Job #1, processed 1670/2000 permutations (83.50%, 5.511418630976877 seconds remaining) Job #1, processed 1680/2000 permutations (84.00%, 5.342585245768229 seconds remaining) Job #1, processed 1690/2000 permutations (84.50%, 5.173767572323952 seconds remaining) Job #1, processed 1700/2000 permutations (85.00%, 5.0060363657334275 seconds remaining) Job #1, processed 1710/2000 permutations (85.50%, 4.837627862629137 seconds remaining) Job #1, processed 1720/2000 permutations (86.00%, 4.66951080810192 seconds remaining) Job #1, processed 1730/2000 permutations (86.50%, 4.504477782056511 seconds remaining) Job #1, processed 1740/2000 permutations (87.00%, 4.336401388562959 seconds remaining) Job #1, processed 1750/2000 permutations (87.50%, 4.168822152273996 seconds remaining) Job #1, processed 1760/2000 permutations (88.00%, 4.001176682385531 seconds remaining) Job #1, processed 1770/2000 permutations (88.50%, 3.833802291902445 seconds remaining) Job #1, processed 1780/2000 permutations (89.00%, 3.6671185600623657 seconds remaining) Job #1, processed 1790/2000 permutations (89.50%, 3.5023627760690017 seconds remaining) Job #1, processed 1800/2000 permutations (90.00%, 3.335512108272976 seconds remaining) Job 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(96.00%, 1.3344888190428414 seconds remaining) Job #1, processed 1930/2000 permutations (96.50%, 1.1675266527758978 seconds remaining) Job #1, processed 1940/2000 permutations (97.00%, 1.0007966670793356 seconds remaining) Job #1, processed 1950/2000 permutations (97.50%, 0.8339726496965457 seconds remaining) Job #1, processed 1960/2000 permutations (98.00%, 0.6671483954604791 seconds remaining) Job #1, processed 1970/2000 permutations (98.50%, 0.5005419339020241 seconds remaining) Job #1, processed 1980/2000 permutations (99.00%, 0.3339619130799265 seconds remaining) Job #1, processed 1990/2000 permutations (99.50%, 0.16696625498671028 seconds remaining) + 1970 detections @@ -391,9 +391,9 @@ Inference with massively univariate model .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 9.758 seconds) + **Total running time of the script:** (1 minutes 16.966 seconds) -**Estimated memory usage:** 1861 MB +**Estimated memory usage:** 1879 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_oasis_vbm.py: diff --git a/dev/_sources/auto_examples/02_decoding/plot_oasis_vbm_space_net.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_oasis_vbm_space_net.rst.txt index be38428ec1d..351150d643a 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_oasis_vbm_space_net.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_oasis_vbm_space_net.rst.txt @@ -249,7 +249,7 @@ SpaceNetRegressor class, to take advantage of a multi-core system. ________________________________________________________________________________ [Memory] Calling nilearn.decoding.space_net.path_scores... - path_scores(, array([[0., ..., 0.], + path_scores(, array([[0., ..., 0.], ..., [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ..., True], ..., @@ -263,12 +263,12 @@ SpaceNetRegressor class, to take advantage of a multi-core system. None, [0.5], array([ 15, ..., 119]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 0), debias=False, verbose=1, screening_percentile=1.2652228933482088) ........./usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/parallel.py:1792: UserWarning: - Persisting input arguments took 1.67s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Persisting input arguments took 1.45s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - _____________________________________________________path_scores - 20.2s, 0.3min + _____________________________________________________path_scores - 24.1s, 0.4min ________________________________________________________________________________ [Memory] Calling nilearn.decoding.space_net.path_scores... - path_scores(, array([[0., ..., 0.], + path_scores(, array([[0., ..., 0.], ..., [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ..., True], ..., @@ -282,12 +282,12 @@ SpaceNetRegressor class, to take advantage of a multi-core system. None, [0.5], array([ 0, ..., 119]), array([15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 1), debias=False, verbose=1, screening_percentile=1.2652228933482088) ........./usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/parallel.py:1792: UserWarning: - Persisting input arguments took 1.67s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Persisting input arguments took 1.46s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - _____________________________________________________path_scores - 19.6s, 0.3min + _____________________________________________________path_scores - 23.2s, 0.4min ________________________________________________________________________________ [Memory] Calling nilearn.decoding.space_net.path_scores... - path_scores(, array([[0., ..., 0.], + path_scores(, array([[0., ..., 0.], ..., [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ..., True], ..., @@ -301,12 +301,12 @@ SpaceNetRegressor class, to take advantage of a multi-core system. None, [0.5], array([ 0, ..., 119]), array([30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 2), debias=False, verbose=1, screening_percentile=1.2652228933482088) ........./usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/parallel.py:1792: UserWarning: - Persisting input arguments took 1.71s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Persisting input arguments took 1.45s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - _____________________________________________________path_scores - 20.0s, 0.3min + _____________________________________________________path_scores - 25.0s, 0.4min ________________________________________________________________________________ [Memory] Calling nilearn.decoding.space_net.path_scores... - path_scores(, array([[0., ..., 0.], + path_scores(, array([[0., ..., 0.], ..., [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ..., True], ..., @@ -320,12 +320,12 @@ SpaceNetRegressor class, to take advantage of a multi-core system. None, [0.5], array([ 0, ..., 119]), array([45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 3), debias=False, verbose=1, screening_percentile=1.2652228933482088) ........./usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/parallel.py:1792: UserWarning: - Persisting input arguments took 1.67s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Persisting input arguments took 1.46s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - _____________________________________________________path_scores - 20.0s, 0.3min + _____________________________________________________path_scores - 23.9s, 0.4min ________________________________________________________________________________ [Memory] Calling nilearn.decoding.space_net.path_scores... - path_scores(, array([[0., ..., 0.], + path_scores(, array([[0., ..., 0.], ..., [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ..., True], ..., @@ -339,12 +339,12 @@ SpaceNetRegressor class, to take advantage of a multi-core system. None, [0.5], array([ 0, ..., 119]), array([60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 4), debias=False, verbose=1, screening_percentile=1.2652228933482088) ........./usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/parallel.py:1792: UserWarning: - Persisting input arguments took 1.67s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Persisting input arguments took 1.45s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - _____________________________________________________path_scores - 20.1s, 0.3min + _____________________________________________________path_scores - 23.9s, 0.4min ________________________________________________________________________________ [Memory] Calling nilearn.decoding.space_net.path_scores... - path_scores(, array([[0., ..., 0.], + path_scores(, array([[0., ..., 0.], ..., [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ..., True], ..., @@ -358,12 +358,12 @@ SpaceNetRegressor class, to take advantage of a multi-core system. None, [0.5], array([ 0, ..., 119]), array([75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 5), debias=False, verbose=1, screening_percentile=1.2652228933482088) ........./usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/parallel.py:1792: UserWarning: - Persisting input arguments took 1.67s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Persisting input arguments took 1.51s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - _____________________________________________________path_scores - 19.7s, 0.3min + _____________________________________________________path_scores - 24.6s, 0.4min ________________________________________________________________________________ [Memory] Calling nilearn.decoding.space_net.path_scores... - path_scores(, array([[0., ..., 0.], + path_scores(, array([[0., ..., 0.], ..., [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ..., True], ..., @@ -379,12 +379,12 @@ SpaceNetRegressor class, to take advantage of a multi-core system. {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 6), debias=False, verbose=1, screening_percentile=1.2652228933482088) ........./usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/parallel.py:1792: UserWarning: - Persisting input arguments took 1.67s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Persisting input arguments took 1.45s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - _____________________________________________________path_scores - 20.0s, 0.3min + _____________________________________________________path_scores - 23.7s, 0.4min ________________________________________________________________________________ [Memory] Calling nilearn.decoding.space_net.path_scores... - path_scores(, array([[0., ..., 0.], + path_scores(, array([[0., ..., 0.], ..., [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ..., True], ..., @@ -400,10 +400,10 @@ SpaceNetRegressor class, to take advantage of a multi-core system. {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 7), debias=False, verbose=1, screening_percentile=1.2652228933482088) ........./usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/parallel.py:1792: UserWarning: - Persisting input arguments took 1.67s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Persisting input arguments took 1.45s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - _____________________________________________________path_scores - 19.8s, 0.3min - Time Elapsed: 205.07490968704224 seconds, 3.4179151614507037 minutes. + _____________________________________________________path_scores - 23.5s, 0.4min + Time Elapsed: 235.76075649261475 seconds, 3.929345941543579 minutes. [NiftiMasker.transform_single_imgs] Loading data from Nifti1Image( shape=(91, 109, 91, 80), affine=array([[ -2., 0., 0., 90.], @@ -486,7 +486,7 @@ Visualize the resulting maps .. rst-class:: sphx-glr-timing - **Total running time of the script:** (3 minutes 34.636 seconds) + **Total running time of the script:** (4 minutes 6.871 seconds) **Estimated memory usage:** 2487 MB diff --git a/dev/_sources/auto_examples/02_decoding/plot_simulated_data.rst.txt b/dev/_sources/auto_examples/02_decoding/plot_simulated_data.rst.txt index 7b1ab707df8..68e349dbe5c 100644 --- a/dev/_sources/auto_examples/02_decoding/plot_simulated_data.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/plot_simulated_data.rst.txt @@ -346,14 +346,14 @@ models, a `coef_` attribute that stores the coefficients **w** estimated * .. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_simulated_data_002.png - :alt: bayesian_ridge: prediction score 0.114, training time: 0.16s + :alt: bayesian_ridge: prediction score 0.114, training time: 0.18s :srcset: /auto_examples/02_decoding/images/sphx_glr_plot_simulated_data_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_simulated_data_003.png - :alt: enet_cv: prediction score 0.528, training time: 0.17s + :alt: enet_cv: prediction score 0.528, training time: 0.20s :srcset: /auto_examples/02_decoding/images/sphx_glr_plot_simulated_data_003.png :class: sphx-glr-multi-img @@ -374,7 +374,7 @@ models, a `coef_` attribute that stores the coefficients **w** estimated * .. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_simulated_data_006.png - :alt: SearchLight: training time: 6.75s + :alt: SearchLight: training time: 7.88s :srcset: /auto_examples/02_decoding/images/sphx_glr_plot_simulated_data_006.png :class: sphx-glr-multi-img @@ -390,11 +390,11 @@ models, a `coef_` attribute that stores the coefficients **w** estimated .. code-block:: none - bayesian_ridge: prediction score 0.114, training time: 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(92.59%, 0.5283574937988733 seconds remaining) Job #1, processed 410/432 voxels (94.91%, 0.36419495153623094 seconds remaining) Job #1, processed 420/432 voxels (97.22%, 0.19835860227464386 seconds remaining) Job #1, processed 430/432 voxels (99.54%, 0.0328008385099315 seconds remaining) SearchLight: training time: 7.88s @@ -428,9 +428,9 @@ slow. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 8.808 seconds) + **Total running time of the script:** (0 minutes 10.406 seconds) -**Estimated memory usage:** 10 MB +**Estimated memory usage:** 9 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_simulated_data.py: diff --git a/dev/_sources/auto_examples/02_decoding/sg_execution_times.rst.txt b/dev/_sources/auto_examples/02_decoding/sg_execution_times.rst.txt index 939239dbf19..896b386be86 100644 --- a/dev/_sources/auto_examples/02_decoding/sg_execution_times.rst.txt +++ b/dev/_sources/auto_examples/02_decoding/sg_execution_times.rst.txt @@ -6,38 +6,38 @@ Computation times ================= -**39:42.909** total execution time for **auto_examples_02_decoding** files: +**38:44.328** total execution time for **auto_examples_02_decoding** files: +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_different_estimators.py` (``plot_haxby_different_estimators.py``) | 09:59.447 | 1329.1 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_different_estimators.py` (``plot_haxby_different_estimators.py``) | 09:29.549 | 1293.9 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_frem.py` (``plot_haxby_frem.py``) | 09:48.706 | 1965.5 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_frem.py` (``plot_haxby_frem.py``) | 07:40.233 | 1952.1 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_oasis_vbm_space_net.py` (``plot_oasis_vbm_space_net.py``) | 03:34.636 | 2487.4 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_oasis_vbm_space_net.py` (``plot_oasis_vbm_space_net.py``) | 04:06.871 | 2487.5 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_full_analysis.py` (``plot_haxby_full_analysis.py``) | 03:02.248 | 1354.9 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_grid_search.py` (``plot_haxby_grid_search.py``) | 03:19.153 | 916.4 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_grid_search.py` (``plot_haxby_grid_search.py``) | 02:52.295 | 916.4 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_full_analysis.py` (``plot_haxby_full_analysis.py``) | 02:44.409 | 1355.3 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_glm_decoding.py` (``plot_haxby_glm_decoding.py``) | 02:31.885 | 916.8 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_glm_decoding.py` (``plot_haxby_glm_decoding.py``) | 02:43.285 | 916.9 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_searchlight_surface.py` (``plot_haxby_searchlight_surface.py``) | 02:00.175 | 916.4 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_searchlight_surface.py` (``plot_haxby_searchlight_surface.py``) | 02:08.983 | 916.5 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_oasis_vbm.py` (``plot_oasis_vbm.py``) | 01:09.758 | 1861.5 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_miyawaki_reconstruction.py` (``plot_miyawaki_reconstruction.py``) | 01:19.917 | 416.8 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_miyawaki_reconstruction.py` (``plot_miyawaki_reconstruction.py``) | 01:05.755 | 416.8 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_oasis_vbm.py` (``plot_oasis_vbm.py``) | 01:16.966 | 1879.4 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_mixed_gambles_frem.py` (``plot_mixed_gambles_frem.py``) | 00:52.765 | 1831.3 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_multiclass.py` (``plot_haxby_multiclass.py``) | 00:55.472 | 3125.1 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_multiclass.py` (``plot_haxby_multiclass.py``) | 00:51.611 | 3114.4 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_mixed_gambles_frem.py` (``plot_mixed_gambles_frem.py``) | 00:54.854 | 1834.8 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_searchlight.py` (``plot_haxby_searchlight.py``) | 00:49.138 | 916.6 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_searchlight.py` (``plot_haxby_searchlight.py``) | 00:52.164 | 916.4 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_anova_svm.py` (``plot_haxby_anova_svm.py``) | 00:26.481 | 916.5 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_anova_svm.py` (``plot_haxby_anova_svm.py``) | 00:28.118 | 916.4 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_miyawaki_encoding.py` (``plot_miyawaki_encoding.py``) | 00:22.236 | 416.8 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_miyawaki_encoding.py` (``plot_miyawaki_encoding.py``) | 00:26.682 | 416.8 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_simulated_data.py` (``plot_simulated_data.py``) | 00:08.808 | 9.5 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_simulated_data.py` (``plot_simulated_data.py``) | 00:10.406 | 9.4 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_stimuli.py` (``plot_haxby_stimuli.py``) | 00:06.964 | 46.0 MB | +| :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_stimuli.py` (``plot_haxby_stimuli.py``) | 00:07.266 | 44.2 MB | +-----------------------------------------------------------------------------------------------------------------------+-----------+-----------+ diff --git a/dev/_sources/auto_examples/03_connectivity/plot_atlas_comparison.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_atlas_comparison.rst.txt index af6e3929995..84a1e7b34e1 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_atlas_comparison.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_atlas_comparison.rst.txt @@ -177,7 +177,7 @@ Extract coordinates on Yeo atlas - parcellations .. code-block:: none - + @@ -350,9 +350,9 @@ Iterate over fetched atlases to extract coordinates - probabilistic .. rst-class:: sphx-glr-timing - **Total running time of the script:** (2 minutes 22.719 seconds) + **Total running time of the script:** (2 minutes 28.225 seconds) -**Estimated memory usage:** 1689 MB +**Estimated memory usage:** 1723 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_atlas_comparison.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_compare_decomposition.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_compare_decomposition.rst.txt index 3d949d283f5..13508aba4aa 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_compare_decomposition.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_compare_decomposition.rst.txt @@ -149,20 +149,16 @@ However, the images need to be in :term:`MNI` template space. [{self.__class__.__name__}.fit] Computing mask /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:693: UserWarning: - Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. + Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. Template whole-brain mask computation - /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/masking.py:647: UserWarning: - - Persisting input arguments took 0.52s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:887: UserWarning: - Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. + Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:693: UserWarning: - Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. + Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. [MultiNiftiMasker.transform] Resampling mask [CanICA] Loading data @@ -301,8 +297,8 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 12.1s, 0.2min - [Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 12.1s + _________________________________________________________fastica - 10.5s, 0.2min + [Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 10.5s ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.004071, ..., 0.000497], @@ -312,7 +308,7 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 12.5s, 0.2min + _________________________________________________________fastica - 11.0s, 0.2min ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.004071, ..., 0.000497], @@ -322,7 +318,7 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 12.1s, 0.2min + _________________________________________________________fastica - 11.9s, 0.2min ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.004071, ..., 0.000497], @@ -332,8 +328,8 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 11.7s, 0.2min - [Parallel(n_jobs=1)]: Done 4 tasks | elapsed: 48.4s + _________________________________________________________fastica - 10.8s, 0.2min + [Parallel(n_jobs=1)]: Done 4 tasks | elapsed: 44.3s ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.004071, ..., 0.000497], @@ -343,7 +339,7 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 11.3s, 0.2min + _________________________________________________________fastica - 11.9s, 0.2min ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.004071, ..., 0.000497], @@ -353,7 +349,7 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 12.0s, 0.2min + _________________________________________________________fastica - 11.4s, 0.2min ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.004071, ..., 0.000497], @@ -363,8 +359,8 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 12.5s, 0.2min - [Parallel(n_jobs=1)]: Done 7 tasks | elapsed: 1.4min + _________________________________________________________fastica - 11.9s, 0.2min + [Parallel(n_jobs=1)]: Done 7 tasks | elapsed: 1.3min ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.004071, ..., 0.000497], @@ -374,7 +370,7 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 11.6s, 0.2min + _________________________________________________________fastica - 10.7s, 0.2min ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.004071, ..., 0.000497], @@ -384,7 +380,7 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 12.5s, 0.2min + _________________________________________________________fastica - 12.1s, 0.2min ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.004071, ..., 0.000497], @@ -394,7 +390,7 @@ However, the images need to be in :term:`MNI` template space. FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. - _________________________________________________________fastica - 11.7s, 0.2min + _________________________________________________________fastica - 13.0s, 0.2min @@ -431,7 +427,7 @@ To visualize we plot the outline of all components on one figure linewidths is ignored by contourf - + @@ -696,19 +692,15 @@ Create a dictionary learning estimator [{self.__class__.__name__}.fit] Computing mask /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:693: UserWarning: - Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. - - /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/masking.py:647: UserWarning: - - Persisting input arguments took 0.52s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:887: UserWarning: - Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. + Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:693: UserWarning: - Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. + Cannot inspect object functools.partial(, mask_type='whole-brain'), ignore list will not work. [MultiNiftiMasker.transform] Resampling mask [DictLearning] Loading data @@ -838,13 +830,13 @@ Create a dictionary learning estimator randomized_svd(array([[-0.001315, ..., 0.004387], ..., [ 0.011243, ..., 0.004194]]), n_components=20, transpose=True, random_state=0, n_iter=3) - ___________________________________________________randomized_svd - 0.7s, 0.0min + ___________________________________________________randomized_svd - 0.8s, 0.0min ________________________________________________________________________________ [Memory] Calling sklearn.decomposition._fastica.fastica... fastica(array([[ 0.00289 , ..., -0.002135], ..., [ 0.005107, ..., -0.012507]]), whiten='arbitrary-variance', fun='cube', random_state=209652396) - __________________________________________________________fastica - 3.1s, 0.1min + __________________________________________________________fastica - 2.7s, 0.0min [DictLearning] Computing initial loadings ________________________________________________________________________________ [Memory] Calling nilearn.decomposition.dict_learning._compute_loadings... @@ -868,7 +860,7 @@ Create a dictionary learning estimator 'n_iter' is deprecated in version 1.1 and will be removed in version 1.4. Use 'max_iter' instead. - _____________________________________________dict_learning_online - 2.7s, 0.0min + _____________________________________________dict_learning_online - 3.0s, 0.1min [Example] Saving results @@ -907,7 +899,7 @@ First plot all DictLearning components together linewidths is ignored by contourf - + @@ -1131,9 +1123,9 @@ to calculate the score per component [-0., ..., 0.]]), per_component=True) /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/decomposition/_base.py:543: UserWarning: - Persisting input arguments took 1.69s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + Persisting input arguments took 1.47s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. - ______________________________________________explained_variance - 57.8s, 1.0min + ______________________________________________explained_variance - 63.0s, 1.0min @@ -1150,9 +1142,9 @@ to calculate the score per component .. rst-class:: sphx-glr-timing - **Total running time of the script:** (6 minutes 39.769 seconds) + **Total running time of the script:** (7 minutes 18.761 seconds) -**Estimated memory usage:** 2530 MB +**Estimated memory usage:** 2591 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_compare_decomposition.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_data_driven_parcellations.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_data_driven_parcellations.rst.txt index 97dbf36d2fd..362a2c5f388 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_data_driven_parcellations.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_data_driven_parcellations.rst.txt @@ -185,13 +185,13 @@ all can be done at once using `Parcellations` object. ..., [-0.005033, ..., -0.000234]]), connectivity=<24256x24256 sparse matrix of type '' with 162682 stored elements in COOrdinate format>, n_clusters=1000, return_distance=False) - ________________________________________________________ward_tree - 2.3s, 0.0min - ____________________________________________________estimator_fit - 2.5s, 0.0min + ________________________________________________________ward_tree - 2.6s, 0.0min + ____________________________________________________estimator_fit - 2.8s, 0.0min /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/masking.py:974: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32. - Ward agglomeration 1000 clusters: 7.98s + Ward agglomeration 1000 clusters: 9.03s [MultiNiftiMasker.fit] Loading data from [/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz]. [{self.__class__.__name__}.fit] Computing mask /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/_utils/cache_mixin.py:241: UserWarning: @@ -216,13 +216,13 @@ all can be done at once using `Parcellations` object. ..., [-0.005033, ..., -0.000234]]), connectivity=<24256x24256 sparse matrix of type '' with 162682 stored elements in COOrdinate format>, n_clusters=2000, return_distance=False) - ________________________________________________________ward_tree - 2.1s, 0.0min - ____________________________________________________estimator_fit - 2.3s, 0.0min + ________________________________________________________ward_tree - 2.3s, 0.0min + ____________________________________________________estimator_fit - 2.5s, 0.0min /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/masking.py:974: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32. - Ward agglomeration 2000 clusters: 5.34s + Ward agglomeration 2000 clusters: 6.15s @@ -356,7 +356,7 @@ clustering by averaging the signal on each parcel. ) ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... - _filter_and_extract(, , + _filter_and_extract(, , { 'background_label': 0, 'clean_kwargs': {}, 'detrend': False, @@ -365,9 +365,9 @@ clustering by averaging the signal on each parcel. 'high_variance_confounds': False, 'keep_masked_labels': True, 'labels': None, - 'labels_img': , + 'labels_img': , 'low_pass': None, - 'mask_img': , + 'mask_img': , 'reports': True, 'smoothing_fwhm': 2.0, 'standardize': False, @@ -380,9 +380,9 @@ clustering by averaging the signal on each parcel. [NiftiLabelsMasker.transform_single_imgs] Smoothing images [NiftiLabelsMasker.transform_single_imgs] Extracting region signals [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals - _______________________________________________filter_and_extract - 1.8s, 0.0min + _______________________________________________filter_and_extract - 2.0s, 0.0min - + @@ -447,7 +447,7 @@ standardization and smoothing, the clusters will form as regions. [-0.00577 , ..., -0.000616]]), MiniBatchKMeans(n_clusters=50, n_init=3, random_state=0)) ____________________________________________________estimator_fit - 0.5s, 0.0min - KMeans clusters: 6.07s + KMeans clusters: 6.60s @@ -551,7 +551,7 @@ with the previous method. ..., [-0.00577 , ..., -0.000616]]), HierarchicalKMeans(n_clusters=50)) - ____________________________________________________estimator_fit - 1.9s, 0.0min + ____________________________________________________estimator_fit - 1.2s, 0.0min /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/masking.py:974: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32. @@ -698,7 +698,7 @@ You can just skip the plotting code, the important part is the figure .. code-block:: none - + @@ -772,7 +772,7 @@ More about ReNA clustering algorithm in the original paper _estimator_fit(array([[-0.005457, ..., -0.005033], ..., [-0.009986, ..., -0.000234]]), - ReNA(mask_img=, + ReNA(mask_img=, memory=Memory(location=nilearn_cache/joblib), n_clusters=5000, scaling=True), 'rena') @@ -781,9 +781,9 @@ More about ReNA clustering algorithm in the original paper recursive_neighbor_agglomeration(array([[-0.005457, ..., -0.005033], ..., [-0.009986, ..., -0.000234]]), - , 5000, n_iter=10, threshold=1e-07, verbose=0) + , 5000, n_iter=10, threshold=1e-07, verbose=0) _________________________________recursive_neighbor_agglomeration - 1.1s, 0.0min - ____________________________________________________estimator_fit - 1.3s, 0.0min + ____________________________________________________estimator_fit - 1.2s, 0.0min [Parcellations.wrapped] loading data from Nifti1Image( shape=(50, 59, 50), affine=array([[ 4., 0., 0., -96.], @@ -800,7 +800,7 @@ More about ReNA clustering algorithm in the original paper ) ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... - _filter_and_extract(, , + _filter_and_extract(, , { 'background_label': 0, 'clean_kwargs': {}, 'detrend': False, @@ -809,9 +809,9 @@ More about ReNA clustering algorithm in the original paper 'high_variance_confounds': False, 'keep_masked_labels': True, 'labels': None, - 'labels_img': , + 'labels_img': , 'low_pass': None, - 'mask_img': , + 'mask_img': , 'reports': True, 'smoothing_fwhm': 2.0, 'standardize': False, @@ -824,8 +824,8 @@ More about ReNA clustering algorithm in the original paper [NiftiLabelsMasker.transform_single_imgs] Smoothing images [NiftiLabelsMasker.transform_single_imgs] Extracting region signals [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals - _______________________________________________filter_and_extract - 1.9s, 0.0min - ReNA 5000 clusters: 7.73s + _______________________________________________filter_and_extract - 2.1s, 0.0min + ReNA 5000 clusters: 8.39s @@ -869,7 +869,7 @@ First, we display the parcellations of the brain image stored in attribute .. code-block:: none - + @@ -955,7 +955,7 @@ obtained with Ward. ) ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... - _filter_and_extract(, , + _filter_and_extract(, , { 'background_label': 0, 'clean_kwargs': {}, 'detrend': False, @@ -964,9 +964,9 @@ obtained with Ward. 'high_variance_confounds': False, 'keep_masked_labels': True, 'labels': None, - 'labels_img': , + 'labels_img': , 'low_pass': None, - 'mask_img': , + 'mask_img': , 'reports': True, 'smoothing_fwhm': 2.0, 'standardize': False, @@ -979,9 +979,9 @@ obtained with Ward. [NiftiLabelsMasker.transform_single_imgs] Smoothing images [NiftiLabelsMasker.transform_single_imgs] Extracting region signals [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals - _______________________________________________filter_and_extract - 1.9s, 0.0min + _______________________________________________filter_and_extract - 2.2s, 0.0min - + @@ -997,9 +997,9 @@ some cases. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 51.622 seconds) + **Total running time of the script:** (0 minutes 56.171 seconds) -**Estimated memory usage:** 2145 MB +**Estimated memory usage:** 2175 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_data_driven_parcellations.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.rst.txt index a609c49b325..52b429a3eba 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.rst.txt @@ -126,7 +126,7 @@ functional datasets linewidths is ignored by contourf - + @@ -189,7 +189,7 @@ more intense non-voxels will be survived. .. code-block:: none - + @@ -295,7 +295,7 @@ connectome relations. .. code-block:: none - + @@ -378,9 +378,9 @@ related to original network given as 4. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (2 minutes 21.065 seconds) + **Total running time of the script:** (2 minutes 45.622 seconds) -**Estimated memory usage:** 1147 MB +**Estimated memory usage:** 1148 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_extract_regions_dictlearning_maps.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_group_level_connectivity.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_group_level_connectivity.rst.txt index 07b34d08680..9eb68bfaae3 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_group_level_connectivity.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_group_level_connectivity.rst.txt @@ -300,7 +300,7 @@ Now we display as a connectome the mean correlation matrix over all children. .. code-block:: none - + @@ -384,7 +384,7 @@ Most of direct connections are weaker than full connections. .. code-block:: none - + @@ -588,9 +588,9 @@ kind should be preferred. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 29.860 seconds) + **Total running time of the script:** (1 minutes 38.834 seconds) -**Estimated memory usage:** 1145 MB +**Estimated memory usage:** 1144 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_group_level_connectivity.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_inverse_covariance_connectome.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_inverse_covariance_connectome.rst.txt index 20d60e0e9f6..dbd4e96bf8b 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_inverse_covariance_connectome.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_inverse_covariance_connectome.rst.txt @@ -113,15 +113,15 @@ Extract time series ________________________________________________________________________________ [Memory] Calling nilearn.image.resampling.resample_img... - resample_img(, interpolation='continuous', target_shape=(50, 59, 50), target_affine=array([[ 4., 0., 0., -96.], + resample_img(, interpolation='continuous', target_shape=(50, 59, 50), target_affine=array([[ 4., 0., 0., -96.], [ 0., 4., 0., -132.], [ 0., 0., 4., -78.], [ 0., 0., 0., 1.]])) - _____________________________________________________resample_img - 1.4s, 0.0min + _____________________________________________________resample_img - 1.3s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , { 'allow_overlap': True, + , { 'allow_overlap': True, 'clean_kwargs': {}, 'detrend': False, 'dtype': None, @@ -141,7 +141,7 @@ Extract time series [NiftiMapsMasker.transform_single_imgs] Loading data from /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiMapsMasker.transform_single_imgs] Extracting region signals [NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals - _______________________________________________filter_and_extract - 1.8s, 0.0min + _______________________________________________filter_and_extract - 2.4s, 0.0min @@ -214,7 +214,7 @@ Display the connectome matrix .. code-block:: none - + @@ -246,7 +246,7 @@ And now display the corresponding graph .. code-block:: none - + @@ -283,7 +283,7 @@ we negate it to get partial correlations .. code-block:: none - + @@ -730,9 +730,9 @@ for more details. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 14.036 seconds) + **Total running time of the script:** (0 minutes 16.026 seconds) -**Estimated memory usage:** 1144 MB +**Estimated memory usage:** 1143 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_inverse_covariance_connectome.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_multi_subject_connectome.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_multi_subject_connectome.rst.txt index 2bcfe288e34..7d1cccd4522 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_multi_subject_connectome.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_multi_subject_connectome.rst.txt @@ -158,11 +158,11 @@ Extracting region signals ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') - __________________________________________high_variance_confounds - 0.8s, 0.0min + __________________________________________high_variance_confounds - 1.3s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , { 'allow_overlap': True, + , { 'allow_overlap': True, 'clean_kwargs': {}, 'detrend': True, 'dtype': None, @@ -189,16 +189,16 @@ Extracting region signals [NiftiMapsMasker.transform_single_imgs] Resampling images [NiftiMapsMasker.transform_single_imgs] Extracting region signals [NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals - _______________________________________________filter_and_extract - 7.6s, 0.1min + _______________________________________________filter_and_extract - 7.4s, 0.1min Processing file /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') - __________________________________________high_variance_confounds - 0.8s, 0.0min + __________________________________________high_variance_confounds - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , { 'allow_overlap': True, + , { 'allow_overlap': True, 'clean_kwargs': {}, 'detrend': True, 'dtype': None, @@ -225,16 +225,16 @@ Extracting region signals [NiftiMapsMasker.transform_single_imgs] Resampling images [NiftiMapsMasker.transform_single_imgs] Extracting region signals [NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals - _______________________________________________filter_and_extract - 7.6s, 0.1min + _______________________________________________filter_and_extract - 7.4s, 0.1min Processing file /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') - __________________________________________high_variance_confounds - 0.8s, 0.0min + __________________________________________high_variance_confounds - 1.3s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , { 'allow_overlap': True, + , { 'allow_overlap': True, 'clean_kwargs': {}, 'detrend': True, 'dtype': None, @@ -261,16 +261,16 @@ Extracting region signals [NiftiMapsMasker.transform_single_imgs] Resampling images [NiftiMapsMasker.transform_single_imgs] Extracting region signals [NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals - _______________________________________________filter_and_extract - 7.8s, 0.1min + _______________________________________________filter_and_extract - 7.4s, 0.1min Processing file /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') - __________________________________________high_variance_confounds - 0.8s, 0.0min + __________________________________________high_variance_confounds - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , { 'allow_overlap': True, + , { 'allow_overlap': True, 'clean_kwargs': {}, 'detrend': True, 'dtype': None, @@ -297,7 +297,7 @@ Extracting region signals [NiftiMapsMasker.transform_single_imgs] Resampling images [NiftiMapsMasker.transform_single_imgs] Extracting region signals [NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals - _______________________________________________filter_and_extract - 7.7s, 0.1min + _______________________________________________filter_and_extract - 7.3s, 0.1min @@ -382,7 +382,7 @@ Computing group-sparse precision matrices [GroupSparseCovarianceCV.fit] Final optimization [GroupSparseCovarianceCV.fit] tolerance reached at iteration number 19: 8.841e-04 ....................[GraphicalLassoCV] Done refinement 1 out of 4: 0s - ....................[GraphicalLassoCV] Done refinement 2 out of 4: 0s + ....................[GraphicalLassoCV] Done refinement 2 out of 4: 1s ....................[GraphicalLassoCV] Done refinement 3 out of 4: 1s ....................[GraphicalLassoCV] Done refinement 4 out of 4: 2s [graphical_lasso] Iteration 0, cost 1.68e+02, dual gap 1.123e+00 @@ -508,9 +508,9 @@ Displaying results .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 40.330 seconds) + **Total running time of the script:** (1 minutes 38.972 seconds) -**Estimated memory usage:** 553 MB +**Estimated memory usage:** 559 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_multi_subject_connectome.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_probabilistic_atlas_extraction.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_probabilistic_atlas_extraction.rst.txt index 70d7d114543..c13106fc2d7 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_probabilistic_atlas_extraction.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_probabilistic_atlas_extraction.rst.txt @@ -113,7 +113,7 @@ Extract the time series memory_level is currently set to 0 but a Memory object has been provided. Setting memory_level to 1. [Memory]0.0s, 0.0min : Loading resample_img... - ________________________________________resample_img cache loaded - 0.0s, 0.0min + ________________________________________resample_img cache loaded - 0.1s, 0.0min [Memory]0.2s, 0.0min : Loading _filter_and_extract... __________________________________filter_and_extract cache loaded - 0.0s, 0.0min @@ -390,20 +390,20 @@ Here, we only include maps 2, 6, 7, 16, and 21 in the report: image @@ -599,7 +599,7 @@ Build and display a correlation matrix .. code-block:: none - + @@ -1054,9 +1054,9 @@ for more details. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 7.065 seconds) + **Total running time of the script:** (0 minutes 9.052 seconds) -**Estimated memory usage:** 377 MB +**Estimated memory usage:** 307 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_probabilistic_atlas_extraction.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_seed_to_voxel_correlation.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_seed_to_voxel_correlation.rst.txt index caf4d794378..9a3b8935c5c 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_seed_to_voxel_correlation.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_seed_to_voxel_correlation.rst.txt @@ -474,9 +474,9 @@ object, that we can save. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 12.604 seconds) + **Total running time of the script:** (0 minutes 15.313 seconds) -**Estimated memory usage:** 737 MB +**Estimated memory usage:** 725 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_seed_to_voxel_correlation.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_signal_extraction.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_signal_extraction.rst.txt index c0b9255ea98..3ef4a1bc825 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_signal_extraction.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_signal_extraction.rst.txt @@ -179,7 +179,7 @@ Using the NiftiLabelsMasker ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , + , { 'background_label': 0, 'clean_kwargs': {}, 'detrend': False, @@ -188,7 +188,7 @@ Using the NiftiLabelsMasker 'high_variance_confounds': False, 'keep_masked_labels': True, 'labels': None, - 'labels_img': , + 'labels_img': , 'low_pass': None, 'mask_img': None, 'reports': True, @@ -259,7 +259,7 @@ Compute and display a correlation matrix .. code-block:: none - + @@ -317,7 +317,7 @@ connectivity matrix looks like without confound removal. ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , + , { 'background_label': 0, 'clean_kwargs': {}, 'detrend': False, @@ -326,7 +326,7 @@ connectivity matrix looks like without confound removal. 'high_variance_confounds': False, 'keep_masked_labels': True, 'labels': None, - 'labels_img': , + 'labels_img': , 'low_pass': None, 'mask_img': None, 'reports': True, @@ -342,7 +342,7 @@ connectivity matrix looks like without confound removal. [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals _______________________________________________filter_and_extract - 1.2s, 0.0min - + @@ -425,7 +425,7 @@ cerebrospinal fluid signal with high-pass filtering. ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , + , { 'background_label': 0, 'clean_kwargs': {}, 'detrend': False, @@ -434,7 +434,7 @@ cerebrospinal fluid signal with high-pass filtering. 'high_variance_confounds': False, 'keep_masked_labels': True, 'labels': None, - 'labels_img': , + 'labels_img': , 'low_pass': None, 'mask_img': None, 'reports': True, @@ -463,7 +463,7 @@ cerebrospinal fluid signal with high-pass filtering. [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals _______________________________________________filter_and_extract - 1.2s, 0.0min - + @@ -549,7 +549,7 @@ strategy. ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , + , { 'background_label': 0, 'clean_kwargs': {}, 'detrend': False, @@ -558,7 +558,7 @@ strategy. 'high_variance_confounds': False, 'keep_masked_labels': True, 'labels': None, - 'labels_img': , + 'labels_img': , 'low_pass': None, 'mask_img': None, 'reports': True, @@ -587,7 +587,7 @@ strategy. [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals _______________________________________________filter_and_extract - 1.2s, 0.0min - + @@ -662,7 +662,7 @@ simple strategy and see its impact. ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , + , { 'background_label': 0, 'clean_kwargs': {}, 'detrend': False, @@ -671,7 +671,7 @@ simple strategy and see its impact. 'high_variance_confounds': False, 'keep_masked_labels': True, 'labels': None, - 'labels_img': , + 'labels_img': , 'low_pass': None, 'mask_img': None, 'reports': True, @@ -700,7 +700,7 @@ simple strategy and see its impact. [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals _______________________________________________filter_and_extract - 1.2s, 0.0min - + @@ -805,7 +805,7 @@ the motion default to basic. [ 0., 0., 2., -72.], [ 0., 0., 0., 1.]]) ) - [Memory]12.0s, 0.2min : Loading _filter_and_extract... + [Memory]12.4s, 0.2min : Loading _filter_and_extract... __________________________________filter_and_extract cache loaded - 0.0s, 0.0min [NiftiLabelsMasker.wrapped] loading data from Nifti1Image( shape=(91, 109, 91), @@ -814,7 +814,7 @@ the motion default to basic. [ 0., 0., 2., -72.], [ 0., 0., 0., 1.]]) ) - [Memory]12.6s, 0.2min : Loading _filter_and_extract... + [Memory]13.0s, 0.2min : Loading _filter_and_extract... __________________________________filter_and_extract cache loaded - 0.0s, 0.0min @@ -830,9 +830,9 @@ References .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 15.127 seconds) + **Total running time of the script:** (0 minutes 15.448 seconds) -**Estimated memory usage:** 486 MB +**Estimated memory usage:** 513 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_signal_extraction.py: diff --git a/dev/_sources/auto_examples/03_connectivity/plot_simulated_connectome.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_simulated_connectome.rst.txt index 633e52cc64a..a6bf34c0bdd 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_simulated_connectome.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_simulated_connectome.rst.txt @@ -188,7 +188,7 @@ Run connectome estimations and plot the results .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.408 seconds) + **Total running time of the script:** (0 minutes 3.665 seconds) **Estimated memory usage:** 9 MB diff --git a/dev/_sources/auto_examples/03_connectivity/plot_sphere_based_connectome.rst.txt b/dev/_sources/auto_examples/03_connectivity/plot_sphere_based_connectome.rst.txt index 082e205cc60..94a51e542c2 100644 --- a/dev/_sources/auto_examples/03_connectivity/plot_sphere_based_connectome.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/plot_sphere_based_connectome.rst.txt @@ -154,7 +154,7 @@ band-pass filtered and **standardized to 1 variance**. ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', - , + , { 'allow_overlap': False, 'clean_kwargs': {'butterworth__padtype': 'even'}, 'detrend': True, @@ -172,7 +172,7 @@ band-pass filtered and **standardized to 1 variance**. [NiftiSpheresMasker.transform_single_imgs] Loading data from /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiSpheresMasker.transform_single_imgs] Extracting region signals [NiftiSpheresMasker.transform_single_imgs] Cleaning extracted signals - _______________________________________________filter_and_extract - 2.2s, 0.0min + _______________________________________________filter_and_extract - 2.5s, 0.0min @@ -274,7 +274,7 @@ We display the graph of connections with .. code-block:: none - + @@ -896,7 +896,7 @@ We just fit our regions signals into the `GraphicalLassoCV` object [GraphicalLassoCV] Done refinement 1 out of 4: 1s [GraphicalLassoCV] Done refinement 2 out of 4: 5s [GraphicalLassoCV] Done refinement 3 out of 4: 11s - [GraphicalLassoCV] Done refinement 4 out of 4: 16s + [GraphicalLassoCV] Done refinement 4 out of 4: 17s /usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/numpy/core/_methods.py:173: RuntimeWarning: invalid value encountered in subtract @@ -994,7 +994,7 @@ and display the graph of connections with `nilearn.plotting.plot_connectome`. .. code-block:: none - + @@ -1041,7 +1041,7 @@ aggregating edge strength from the graph would help. Use the function .. code-block:: none - + @@ -1112,7 +1112,7 @@ one for the positive and one for the negative structure. .. code-block:: none - + @@ -1262,7 +1262,7 @@ We repeat the same steps for Dosenbach's atlas. invalid value encountered in subtract - + @@ -1309,9 +1309,9 @@ See Also .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 0.304 seconds) + **Total running time of the script:** (1 minutes 3.194 seconds) -**Estimated memory usage:** 578 MB +**Estimated memory usage:** 577 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_sphere_based_connectome.py: diff --git a/dev/_sources/auto_examples/03_connectivity/sg_execution_times.rst.txt b/dev/_sources/auto_examples/03_connectivity/sg_execution_times.rst.txt index ef4b337f0ea..9b0f666dd98 100644 --- a/dev/_sources/auto_examples/03_connectivity/sg_execution_times.rst.txt +++ b/dev/_sources/auto_examples/03_connectivity/sg_execution_times.rst.txt @@ -6,30 +6,30 @@ Computation times ================= -**17:17.907** total execution time for **auto_examples_03_connectivity** files: +**18:49.283** total execution time for **auto_examples_03_connectivity** files: +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_compare_decomposition.py` (``plot_compare_decomposition.py``) | 06:39.769 | 2529.9 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_compare_decomposition.py` (``plot_compare_decomposition.py``) | 07:18.761 | 2591.4 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_atlas_comparison.py` (``plot_atlas_comparison.py``) | 02:22.719 | 1689.5 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_extract_regions_dictlearning_maps.py` (``plot_extract_regions_dictlearning_maps.py``) | 02:45.622 | 1147.5 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_extract_regions_dictlearning_maps.py` (``plot_extract_regions_dictlearning_maps.py``) | 02:21.065 | 1147.4 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_atlas_comparison.py` (``plot_atlas_comparison.py``) | 02:28.225 | 1723.2 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_multi_subject_connectome.py` (``plot_multi_subject_connectome.py``) | 01:40.330 | 552.7 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_multi_subject_connectome.py` (``plot_multi_subject_connectome.py``) | 01:38.972 | 558.7 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_group_level_connectivity.py` (``plot_group_level_connectivity.py``) | 01:29.860 | 1145.2 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_group_level_connectivity.py` (``plot_group_level_connectivity.py``) | 01:38.834 | 1144.4 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_sphere_based_connectome.py` (``plot_sphere_based_connectome.py``) | 01:00.304 | 577.8 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_sphere_based_connectome.py` (``plot_sphere_based_connectome.py``) | 01:03.194 | 577.3 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_data_driven_parcellations.py` (``plot_data_driven_parcellations.py``) | 00:51.622 | 2144.6 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_data_driven_parcellations.py` (``plot_data_driven_parcellations.py``) | 00:56.171 | 2175.2 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_signal_extraction.py` (``plot_signal_extraction.py``) | 00:15.127 | 486.5 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_inverse_covariance_connectome.py` (``plot_inverse_covariance_connectome.py``) | 00:16.026 | 1143.4 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_inverse_covariance_connectome.py` (``plot_inverse_covariance_connectome.py``) | 00:14.036 | 1143.5 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_signal_extraction.py` (``plot_signal_extraction.py``) | 00:15.448 | 513.0 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_seed_to_voxel_correlation.py` (``plot_seed_to_voxel_correlation.py``) | 00:12.604 | 736.7 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_seed_to_voxel_correlation.py` (``plot_seed_to_voxel_correlation.py``) | 00:15.313 | 724.8 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_probabilistic_atlas_extraction.py` (``plot_probabilistic_atlas_extraction.py``) | 00:07.065 | 376.6 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_probabilistic_atlas_extraction.py` (``plot_probabilistic_atlas_extraction.py``) | 00:09.052 | 306.7 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ -| :ref:`sphx_glr_auto_examples_03_connectivity_plot_simulated_connectome.py` (``plot_simulated_connectome.py``) | 00:03.408 | 9.0 MB | +| :ref:`sphx_glr_auto_examples_03_connectivity_plot_simulated_connectome.py` (``plot_simulated_connectome.py``) | 00:03.665 | 9.0 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------+ diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_adhd_dmn.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_adhd_dmn.rst.txt index 36bf8d80bcc..276452a0718 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_adhd_dmn.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_adhd_dmn.rst.txt @@ -298,9 +298,9 @@ We have several ways to access the report: .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 18.341 seconds) + **Total running time of the script:** (0 minutes 19.864 seconds) -**Estimated memory usage:** 586 MB +**Estimated memory usage:** 588 MB .. _sphx_glr_download_auto_examples_04_glm_first_level_plot_adhd_dmn.py: diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_bids_features.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_bids_features.rst.txt index 101f926d599..e29707dddf3 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_bids_features.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_bids_features.rst.txt @@ -570,9 +570,9 @@ View the generated files .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 43.291 seconds) + **Total running time of the script:** (0 minutes 45.126 seconds) -**Estimated memory usage:** 526 MB +**Estimated memory usage:** 428 MB .. _sphx_glr_download_auto_examples_04_glm_first_level_plot_bids_features.py: diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_design_matrix.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_design_matrix.rst.txt index 222c4f39474..fb81d8be6a0 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_design_matrix.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_design_matrix.rst.txt @@ -254,7 +254,7 @@ Here are the three designs side by side. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.135 seconds) + **Total running time of the script:** (0 minutes 3.296 seconds) **Estimated memory usage:** 9 MB diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_fiac_analysis.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_fiac_analysis.rst.txt index 572fbaab867..0c2e4d7b3ad 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_fiac_analysis.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_fiac_analysis.rst.txt @@ -321,7 +321,7 @@ Compute the statistics for the first session. .. code-block:: none - + @@ -355,7 +355,7 @@ Compute the statistics for the second session. .. code-block:: none - + @@ -468,7 +468,7 @@ We have several ways to access the report: .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 56.851 seconds) + **Total running time of the script:** (0 minutes 57.709 seconds) **Estimated memory usage:** 367 MB diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_fir_model.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_fir_model.rst.txt index e1d1216f211..5d6d92a510e 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_fir_model.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_fir_model.rst.txt @@ -272,9 +272,9 @@ conditions. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 11.907 seconds) + **Total running time of the script:** (0 minutes 12.435 seconds) -**Estimated memory usage:** 132 MB +**Estimated memory usage:** 194 MB .. _sphx_glr_download_auto_examples_04_glm_first_level_plot_fir_model.py: diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_first_level_details.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_first_level_details.rst.txt index d8d438b70aa..7b06f44b417 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_first_level_details.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_first_level_details.rst.txt @@ -1298,9 +1298,9 @@ are specific to this dataset and may vary with other ones. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (2 minutes 29.489 seconds) + **Total running time of the script:** (2 minutes 34.906 seconds) -**Estimated memory usage:** 141 MB +**Estimated memory usage:** 195 MB .. _sphx_glr_download_auto_examples_04_glm_first_level_plot_first_level_details.py: diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_fixed_effects.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_fixed_effects.rst.txt index 9593030ed37..cbb1a02c914 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_fixed_effects.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_fixed_effects.rst.txt @@ -182,7 +182,7 @@ Statistics for the first session .. code-block:: none - + @@ -217,7 +217,7 @@ Statistics for the second session .. code-block:: none - + @@ -264,7 +264,7 @@ Fixed effects statistics The behavior of this function will be changed in release 0.13 to have an additionalreturn value 'fixed_fx_z_score_img' by default. Please set return_z_score to True. - + @@ -292,9 +292,9 @@ t statistic (not a z statistic) .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 15.096 seconds) + **Total running time of the script:** (0 minutes 15.489 seconds) -**Estimated memory usage:** 548 MB +**Estimated memory usage:** 549 MB .. _sphx_glr_download_auto_examples_04_glm_first_level_plot_fixed_effects.py: diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_hrf.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_hrf.rst.txt index b36b570c523..c6e847b3b54 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_hrf.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_hrf.rst.txt @@ -255,7 +255,7 @@ Sample and plot response functions .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 1.666 seconds) + **Total running time of the script:** (0 minutes 1.697 seconds) **Estimated memory usage:** 9 MB diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_localizer_surface_analysis.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_localizer_surface_analysis.rst.txt index 5e1f5d5b044..1b218bf7779 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_localizer_surface_analysis.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_localizer_surface_analysis.rst.txt @@ -517,9 +517,9 @@ Finally, we create contrast-specific maps and plot them. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 14.405 seconds) + **Total running time of the script:** (0 minutes 14.774 seconds) -**Estimated memory usage:** 233 MB +**Estimated memory usage:** 132 MB .. _sphx_glr_download_auto_examples_04_glm_first_level_plot_localizer_surface_analysis.py: diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_predictions_residuals.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_predictions_residuals.rst.txt index a4f99b351ee..52fa4d7bb93 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_predictions_residuals.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_predictions_residuals.rst.txt @@ -130,7 +130,7 @@ Calculate and plot contrast .. code-block:: none - + @@ -324,7 +324,7 @@ we can use an F-test as shown in the next section. .. code-block:: none - + @@ -375,16 +375,16 @@ all the other columns in the design matrix such as drift and motion. .. code-block:: none - + .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 29.529 seconds) + **Total running time of the script:** (0 minutes 29.388 seconds) -**Estimated memory usage:** 615 MB +**Estimated memory usage:** 710 MB .. _sphx_glr_download_auto_examples_04_glm_first_level_plot_predictions_residuals.py: diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_spm_multimodal_faces.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_spm_multimodal_faces.rst.txt index d00b3b97227..66bb1739d18 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_spm_multimodal_faces.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_spm_multimodal_faces.rst.txt @@ -388,7 +388,7 @@ also displays some responses in the frontal lobe. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 28.279 seconds) + **Total running time of the script:** (1 minutes 32.893 seconds) **Estimated memory usage:** 877 MB diff --git a/dev/_sources/auto_examples/04_glm_first_level/plot_write_events_file.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/plot_write_events_file.rst.txt index b497f11bfb1..44c2f2394d3 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/plot_write_events_file.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/plot_write_events_file.rst.txt @@ -373,7 +373,7 @@ Optionally, the events can be visualized using the .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.611 seconds) + **Total running time of the script:** (0 minutes 3.816 seconds) **Estimated memory usage:** 9 MB diff --git a/dev/_sources/auto_examples/04_glm_first_level/sg_execution_times.rst.txt b/dev/_sources/auto_examples/04_glm_first_level/sg_execution_times.rst.txt index d2a40827400..064be745050 100644 --- a/dev/_sources/auto_examples/04_glm_first_level/sg_execution_times.rst.txt +++ b/dev/_sources/auto_examples/04_glm_first_level/sg_execution_times.rst.txt @@ -6,30 +6,30 @@ Computation times ================= -**07:15.600** total execution time for **auto_examples_04_glm_first_level** files: +**07:31.392** total execution time for **auto_examples_04_glm_first_level** files: +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_first_level_details.py` (``plot_first_level_details.py``) | 02:29.489 | 140.8 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_first_level_details.py` (``plot_first_level_details.py``) | 02:34.906 | 195.0 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_spm_multimodal_faces.py` (``plot_spm_multimodal_faces.py``) | 01:28.279 | 877.3 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_spm_multimodal_faces.py` (``plot_spm_multimodal_faces.py``) | 01:32.893 | 876.9 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_fiac_analysis.py` (``plot_fiac_analysis.py``) | 00:56.851 | 367.1 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_fiac_analysis.py` (``plot_fiac_analysis.py``) | 00:57.709 | 367.1 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_bids_features.py` (``plot_bids_features.py``) | 00:43.291 | 526.1 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_bids_features.py` (``plot_bids_features.py``) | 00:45.126 | 428.4 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_predictions_residuals.py` (``plot_predictions_residuals.py``) | 00:29.529 | 615.2 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_predictions_residuals.py` (``plot_predictions_residuals.py``) | 00:29.388 | 710.3 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_adhd_dmn.py` (``plot_adhd_dmn.py``) | 00:18.341 | 586.3 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_adhd_dmn.py` (``plot_adhd_dmn.py``) | 00:19.864 | 588.2 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_fixed_effects.py` (``plot_fixed_effects.py``) | 00:15.096 | 547.5 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_fixed_effects.py` (``plot_fixed_effects.py``) | 00:15.489 | 549.3 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_localizer_surface_analysis.py` (``plot_localizer_surface_analysis.py``) | 00:14.405 | 232.5 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_localizer_surface_analysis.py` (``plot_localizer_surface_analysis.py``) | 00:14.774 | 132.5 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_fir_model.py` (``plot_fir_model.py``) | 00:11.907 | 132.3 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_fir_model.py` (``plot_fir_model.py``) | 00:12.435 | 194.0 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_write_events_file.py` (``plot_write_events_file.py``) | 00:03.611 | 9.0 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_write_events_file.py` (``plot_write_events_file.py``) | 00:03.816 | 9.0 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_design_matrix.py` (``plot_design_matrix.py``) | 00:03.135 | 9.2 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_design_matrix.py` (``plot_design_matrix.py``) | 00:03.296 | 9.1 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ -| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_hrf.py` (``plot_hrf.py``) | 00:01.666 | 9.0 MB | +| :ref:`sphx_glr_auto_examples_04_glm_first_level_plot_hrf.py` (``plot_hrf.py``) | 00:01.697 | 9.0 MB | +------------------------------------------------------------------------------------------------------------------------------+-----------+----------+ diff --git a/dev/_sources/auto_examples/05_glm_second_level/plot_oasis.rst.txt b/dev/_sources/auto_examples/05_glm_second_level/plot_oasis.rst.txt index b5ba1a6bf90..92226934913 100644 --- a/dev/_sources/auto_examples/05_glm_second_level/plot_oasis.rst.txt +++ b/dev/_sources/auto_examples/05_glm_second_level/plot_oasis.rst.txt @@ -247,8 +247,8 @@ also smooth a little bit to improve statistical behavior. .. raw:: html
- 
SecondLevelModel(mask_img=<nibabel.nifti1.Nifti1Image object at 0x7fc910066760>,
-                     smoothing_fwhm=2.0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -788,7 +788,7 @@

Visualizing one volume in a 4D file
plotting.plot_stat_map(first_rsn)
-plot 3d and 4d niimg
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc9110b6fa0>
+plot 3d and 4d niimg
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fac7c912d00>

@@ -860,7 +860,7 @@

Looping through selected volumes in a 4D filenilearn.image.concat_imgs to group a list of 3D images into a 4D image.

-

Total running time of the script: (0 minutes 16.097 seconds)

+

Total running time of the script: (0 minutes 19.974 seconds)

Estimated memory usage: 127 MB

-plot decoding tutorial
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc910ff8130>
+plot decoding tutorial
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fac7c4451f0>

@@ -1132,122 +1132,122 @@

Turning the weights into a nifti imageprint(coef_) -
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+   1.95406451e-02  3.57882015e-02  4.89306332e-02  3.82973279e-02
+   6.23919778e-02  6.13675702e-02 -1.68751106e-02  1.66514417e-02
+   3.35522681e-02 -1.80212292e-02  4.46410864e-02 -3.53244442e-02
+  -3.67292203e-02 -4.62254655e-03  4.86828817e-02  3.39667366e-02
+   6.21715332e-03  1.73611952e-02  2.01698042e-02  2.17097443e-02
+   2.91412961e-02  2.37777459e-02  4.84698989e-02 -9.22624556e-03
+  -2.82638059e-02 -2.13781349e-02  1.80790135e-03  4.79687913e-02
+  -9.78898501e-03  1.11431178e-02 -1.65020129e-02 -2.89090440e-02
+   2.42849793e-02 -1.22346516e-02 -2.92870977e-02 -2.89845050e-02
+  -3.39532204e-02 -3.65278556e-03  2.65324161e-02  4.58043283e-02
+  -5.93381835e-02 -2.13630673e-02 -3.09406538e-02  5.50179590e-02
+  -3.38816404e-02  6.12613834e-03  1.41483557e-02  1.10215898e-02
+   5.33811916e-02 -2.12339103e-02  6.37423115e-03 -1.13075791e-02
+  -2.64225988e-02 -2.22399516e-02 -5.31920798e-02 -3.98653667e-02
+  -1.29727849e-01 -3.28091799e-02 -2.89710724e-02 -9.13467709e-03
+  -7.28720612e-03 -3.71052046e-02 -6.34907877e-02  2.04387862e-03
+  -8.26794234e-02 -6.71215532e-02 -2.29125338e-03 -2.33451680e-02
+   1.77914470e-02 -8.74667788e-02 -2.76490603e-03 -4.38275449e-02
+  -1.28051207e-02  2.78034594e-02 -4.32695506e-02 -3.22687161e-02
+  -2.28029479e-02 -2.57415240e-02  2.03623759e-02 -9.90252814e-03
+  -3.15034303e-02 -1.81420286e-02 -1.12328538e-03 -4.17432276e-02
+  -6.23479550e-02  2.54766092e-04 -6.73679541e-02  6.53966163e-02
+   1.06521738e-02  2.21983012e-02 -1.98726838e-02 -1.85519589e-02
+   4.05703432e-02 -3.02837292e-02 -8.10051870e-02 -7.42460038e-02
+  -4.93849785e-02 -1.01771273e-02  1.09406470e-02 -4.49253905e-02
+   2.92747884e-02  7.05302617e-03  5.07538971e-03 -4.84049694e-03
+   2.48741019e-03  3.00654996e-02 -2.63084271e-03  4.64685479e-03
+   7.90210013e-02  1.04858093e-02  1.68080026e-02 -4.36719270e-02
+  -1.08856432e-02  2.10241943e-02 -4.41965786e-02  3.16485450e-03
+   6.98670736e-02  8.61629338e-02  4.96233351e-02  6.03897625e-03
+   5.56494665e-02 -2.98920784e-02  4.13032548e-03 -3.21952280e-02
+  -3.14991326e-02 -5.31276242e-02  2.67257924e-02  3.14428646e-02
+   6.67113750e-03 -1.28703524e-02  2.20151205e-02  5.68524505e-02
+   2.25603273e-02 -2.04617651e-02  5.10333778e-03  2.85356881e-02
+  -1.81663697e-02 -8.48429650e-03 -3.18824429e-02 -1.18497140e-02
+  -4.10844637e-02  3.11777631e-02  9.63477303e-03 -8.25915542e-03
+  -3.12227047e-02  8.57644367e-03 -9.70201488e-03  1.32373185e-02
+   4.06448469e-02  8.23406757e-03 -3.27356354e-02 -4.33877093e-03
+  -1.75530634e-02  6.88838512e-03  3.45131464e-02  7.03299980e-02
+   2.16784701e-02  5.32229897e-03  8.17563549e-02  6.40063765e-02
+  -2.31138936e-03 -1.17557516e-02  1.75889119e-01  3.18128890e-02
+  -3.15888172e-02  3.34028524e-02  2.22781246e-02  1.00231446e-02
+  -4.74912216e-02 -2.12756955e-02 -3.98717655e-02 -6.04069569e-02
+  -4.65060608e-02  1.03003120e-02 -3.05711558e-04  1.80743735e-02
+  -1.75451987e-02 -8.72591028e-02  1.00662664e-01  4.46109943e-03
+   7.46869488e-02 -6.13411441e-02  2.81703843e-02 -1.40978793e-02
+   3.14638185e-02 -1.63834897e-02  3.66533173e-02 -5.15666693e-03
+   1.45093382e-02  6.35867368e-02  2.34598550e-02  8.81062148e-02
+   6.15344326e-02 -1.39361581e-02  2.07246929e-02 -3.15457439e-03
+   5.15424222e-02 -2.88767715e-02  1.60263173e-02  2.09701837e-02
+  -3.29172453e-02 -2.59460569e-02 -5.60401570e-02 -3.64627826e-02
+   1.12882160e-02  2.17267472e-02 -1.51638185e-02 -7.82884895e-03
+   2.42549716e-02  9.47012371e-02 -2.63034234e-02  1.17323752e-04
+  -5.24175603e-03  4.17987979e-02  8.85681175e-02  6.23648257e-03
+   1.86600445e-02  1.54629553e-02  3.50543487e-03  6.20600811e-03
+  -1.19790136e-02  1.59526686e-02  7.12126289e-03 -8.93192974e-02
+  -3.54345067e-03  1.23478749e-02  3.03928100e-02 -2.37294919e-02
+  -3.82791515e-02 -4.98746332e-02  4.66896396e-02 -1.23292353e-02
+  -1.10332642e-02  2.18105228e-02  2.18719684e-02  2.63538207e-02
+   1.05279990e-02  1.84618082e-02  8.36035958e-04 -6.65212529e-03
+   3.49396921e-02  1.49353131e-02 -1.11598376e-02  6.69097818e-03
+  -2.00059153e-02 -3.99015334e-02  3.01872978e-02 -1.09867013e-02
+  -4.11780764e-02  2.72052376e-02  1.16425770e-02 -1.55502397e-02
+   3.27700158e-02  3.95493921e-02  8.48722399e-03  2.19935247e-02
+  -9.88663671e-03 -3.61421471e-02 -4.77021449e-02  1.90074377e-02
+  -5.58286786e-02 -3.31738578e-02 -2.24914328e-02 -3.36175567e-02
+  -4.07354767e-02  1.08861874e-02  1.12811065e-02  7.63144499e-02
+   4.04801659e-03  3.07014132e-02  2.89177166e-02  4.71610048e-03
+   5.13384180e-02 -4.10364432e-02  1.23255240e-03 -2.50403779e-02
+   5.85903815e-02 -1.04965650e-01 -4.41705748e-02  1.18520202e-02
+  -5.83204038e-02 -4.82244633e-02  9.17658613e-03  1.03259613e-02
+  -5.09184442e-03 -3.23390265e-02 -3.19386688e-02 -1.53771394e-02
+  -5.21211851e-02  1.55619488e-02  2.93484466e-02 -1.92527985e-02
+   1.76694768e-02  2.67991926e-02  5.76553739e-02 -1.38163827e-02
+   2.60399830e-02  1.50401307e-02  1.27425133e-02 -2.29243926e-02
+  -1.06664941e-02  9.81930805e-03 -4.77511796e-02  1.64244040e-02]]
 

 

 
It’s a numpy array with only one coefficient per voxel:

@@ -1316,7 +1316,7 @@ 
Plotting the SVM weightsSpaceNet: decoding with spatial structure for better maps

 
 

-
Total running time of the script: (0 minutes 39.634 seconds)

+
Total running time of the script: (0 minutes 43.816 seconds)

 
Estimated memory usage:  916 MB
-plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc911290c10>
+plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fac7ca2cd00>
 

 

 
This is not a very pretty plot. We just used the simplest possible
@@ -754,7 +754,7 @@ 
Simple image manipulation: smoothingsmooth_anat_img
-
<nibabel.nifti1.Nifti1Image object at 0x7fc9175da5e0>
+
<nibabel.nifti1.Nifti1Image object at 0x7fac7ca23130>
 

 

 
This is an in-memory object. We can pass it to nilearn function, for
@@ -762,7 +762,7 @@ 
Simple image manipulation: smoothing
plotting.plot_img(smooth_anat_img)
-plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc914faa2b0>
+plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fac7c95ec40>
 

 

 
We could also pass it to the smoothing function

@@ -770,7 +770,7 @@ 
Simple image manipulation: smoothingplotting.plot_img(more_smooth_anat_img)
-plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc911215d60>
+plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fac7c966670>

@@ -792,8 +792,8 @@

Saving results to a fileTotal running time of the script: (0 minutes 6.310 seconds)

-

Estimated memory usage: 265 MB

+

Total running time of the script: (0 minutes 6.202 seconds)

+

Estimated memory usage: 255 MB

 -plot python 101
[<matplotlib.lines.Line2D object at 0x7fc91757c6a0>]
+plot python 101
[<matplotlib.lines.Line2D object at 0x7fac86caf490>]
 

 

-
Total running time of the script: (0 minutes 0.714 seconds)

+
Total running time of the script: (0 minutes 0.855 seconds)

 
Estimated memory usage:  10 MB

@@ -1879,8 +1879,8 @@

Impact of plot parameters on visualization
-

Total running time of the script: (0 minutes 41.950 seconds)

-

Estimated memory usage: 424 MB

+

Total running time of the script: (0 minutes 35.803 seconds)

+

Estimated memory usage: 460 MB

 -plot atlas
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc9106f2d00>
+plot atlas
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fac7bedc400>

@@ -744,7 +744,7 @@

Visualizing the Juelich atlas
plotting.plot_roi(atlas_ju_filename, title="Juelich atlas")
-plot atlas
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc917f757f0>
+plot atlas
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fac7c7de970>

@@ -771,8 +771,8 @@

Visualizing the Juelich atlas with contoursplotting.show() -plot atlas

Total running time of the script: (1 minutes 22.608 seconds)

-

Estimated memory usage: 443 MB

+plot atlas

Total running time of the script: (1 minutes 46.368 seconds)

+

Estimated memory usage: 457 MB

-

Total running time of the script: (0 minutes 9.327 seconds)

-

Estimated memory usage: 913 MB

+

Total running time of the script: (0 minutes 11.000 seconds)

+

Estimated memory usage: 963 MB

 -Accent, Blues, BrBG, BuGn, BuPu, CMRmap, Dark2, GnBu, Greens, Greys, OrRd, Oranges, PRGn, Paired, Pastel1, Pastel2, PiYG, PuBu, PuBuGn, PuOr, PuRd, Purples, RdBu, RdGy, RdPu, RdYlBu, RdYlGn, Reds, Set1, Set2, Set3, Spectral, Wistia, YlGn, YlGnBu, YlOrBr, YlOrRd, afmhot, autumn, binary, bone, brg, bwr, cool, coolwarm, copper, cubehelix, flag, gist_earth, gist_gray, gist_heat, gist_ncar, gist_rainbow, gist_stern, gist_yarg, gnuplot, gnuplot2, gray, hot, hsv, jet, nipy_spectral, ocean, pink, prism, rainbow, seismic, spring, summer, tab10, tab20, tab20b, tab20c, terrain, winter

Total running time of the script: (0 minutes 3.251 seconds)

-

Estimated memory usage: 18 MB

+Accent, Blues, BrBG, BuGn, BuPu, CMRmap, Dark2, GnBu, Greens, Greys, OrRd, Oranges, PRGn, Paired, Pastel1, Pastel2, PiYG, PuBu, PuBuGn, PuOr, PuRd, Purples, RdBu, RdGy, RdPu, RdYlBu, RdYlGn, Reds, Set1, Set2, Set3, Spectral, Wistia, YlGn, YlGnBu, YlOrBr, YlOrRd, afmhot, autumn, binary, bone, brg, bwr, cool, coolwarm, copper, cubehelix, flag, gist_earth, gist_gray, gist_heat, gist_ncar, gist_rainbow, gist_stern, gist_yarg, gnuplot, gnuplot2, gray, hot, hsv, jet, nipy_spectral, ocean, pink, prism, rainbow, seismic, spring, summer, tab10, tab20, tab20b, tab20c, terrain, winter

Total running time of the script: (0 minutes 3.692 seconds)

+

Estimated memory usage: 19 MB

-plot demo glass brain
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fc910ffe910>
+plot demo glass brain
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fac7c7f5730>

@@ -741,7 +741,7 @@

Glass brain plotting: black background) -plot demo glass brain
<nilearn.plotting.displays._projectors.XZProjector object at 0x7fc910a1f430>
+plot demo glass brain
<nilearn.plotting.displays._projectors.XZProjector object at 0x7fac7c149040>
 

 

 
@@ -757,7 +757,7 @@ 
Glass brain plotting: Hemispheric sagittal cutsplotting.show()
-plot demo glass brain

Total running time of the script: (0 minutes 3.331 seconds)

+plot demo glass brain

Total running time of the script: (0 minutes 4.101 seconds)

Estimated memory usage: 18 MB

-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fc8fc5a5670>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fac67ccea90>
 

 

 
The same figure, with a colorbar, can be produced by
@@ -756,7 +756,7 @@ 
Demo glass brain plotting
plot_glass_brain(stat_img, threshold=3, colorbar=True)
-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fc9108dec10>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fac7c733dc0>
 

 

 
Here, we show how to set a black background, and we only view sagittal and
@@ -771,7 +771,7 @@ 
Demo glass brain plotting)
-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.XZProjector object at 0x7fc910fb6bb0>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.XZProjector object at 0x7fac7bedc9a0>
 

 

 
We can also plot the sign of the activation by setting plot_abs=False.
@@ -783,7 +783,7 @@ 
Demo glass brain plotting)
-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.YZProjector object at 0x7fc910c245e0>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.YZProjector object at 0x7fac7c072400>
 

 

 
Setting plot_abs=True and display_mode='yx' (returns a
@@ -793,7 +793,7 @@ 
Demo glass brain plotting)
-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.YXProjector object at 0x7fc910908b50>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.YXProjector object at 0x7fac7c558940>
 

 

 
We can control the limits of the colormap and colorbar by setting vmin
@@ -812,7 +812,7 @@ 
Demo glass brain plotting)
-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.YZProjector object at 0x7fc8fe72fcd0>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.YZProjector object at 0x7fac7c173190>
 

 

 
Here we set vmin to the threshold to use the full color range instead of
@@ -829,7 +829,7 @@ 
Demo glass brain plotting)
-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.YZProjector object at 0x7fc910ada5e0>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.YZProjector object at 0x7fac7bee4760>
 

 

 
@@ -850,7 +850,7 @@ 
Different projections for the left and right hemispheres)
-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.LZRProjector object at 0x7fc910f303d0>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.LZRProjector object at 0x7fac7c072400>
 

 

 
display_mode='lyrz' returns a
@@ -865,7 +865,7 @@ 
Different projections for the left and right hemispheres)
-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.LYRZProjector object at 0x7fc90efc3d30>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.LYRZProjector object at 0x7fac7c4a0f10>
 

 

 
If you are only interested in single projections, you can set
@@ -885,7 +885,7 @@ 
Different projections for the left and right hemispheres)
-plot demo glass brain extensive
<nilearn.plotting.displays._projectors.XProjector object at 0x7fc8fe2c8b80>
+plot demo glass brain extensive
<nilearn.plotting.displays._projectors.XProjector object at 0x7fac691ef280>
 

 

 
@@ -1020,7 +1020,7 @@ 
Display contour projections in both hemispheresplotting.show()
-plot demo glass brain extensive

Total running time of the script: (0 minutes 26.643 seconds)

+plot demo glass brain extensive

Total running time of the script: (0 minutes 33.498 seconds)

Estimated memory usage: 9 MB

-plot demo more plotting
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc91032bf10>
+plot demo more plotting
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fac7c512670>
 

 

 
@@ -796,7 +796,7 @@ 
Visualizing in - single view ‘axial’ with number of cuts=5)
-plot demo more plotting
<nilearn.plotting.displays._slicers.ZSlicer object at 0x7fc90fb0ae20>
+plot demo more plotting
<nilearn.plotting.displays._slicers.ZSlicer object at 0x7fac7a6fcaf0>
 

 

 
@@ -814,7 +814,7 @@ 
Visualizing in - single view ‘sagittal’ with only two slices)
-plot demo more plotting
<nilearn.plotting.displays._slicers.XSlicer object at 0x7fc9111136a0>
+plot demo more plotting
<nilearn.plotting.displays._slicers.XSlicer object at 0x7fac7bc12a90>
 

 

 
@@ -835,7 +835,7 @@ 
Visualizing in - ‘coronal’ view with single cut)
-plot demo more plotting
<nilearn.plotting.displays._slicers.YSlicer object at 0x7fc91113cb50>
+plot demo more plotting
<nilearn.plotting.displays._slicers.YSlicer object at 0x7fac7c4746a0>
 

 

 
@@ -853,7 +853,7 @@ 
Visualizing without a colorbar on the right side)
-plot demo more plotting
<nilearn.plotting.displays._slicers.ZSlicer object at 0x7fc91029e160>
+plot demo more plotting
<nilearn.plotting.displays._slicers.ZSlicer object at 0x7fac7c7333a0>
 

 

 
@@ -874,7 +874,7 @@ 
Visualize in - two views ‘sagittal’ and ‘axial’ with given coordinat
 )
-plot demo more plotting
<nilearn.plotting.displays._slicers.XZSlicer object at 0x7fc91113ca60>
+plot demo more plotting
<nilearn.plotting.displays._slicers.XZSlicer object at 0x7fac7c474460>
 

 

 
@@ -892,7 +892,7 @@ 
Changing the views to ‘coronal’, ‘sagittal’ views with coordinates)
-plot demo more plotting
<nilearn.plotting.displays._slicers.YXSlicer object at 0x7fc91025d4f0>
+plot demo more plotting
<nilearn.plotting.displays._slicers.YXSlicer object at 0x7fac7c393070>
 

 

 
@@ -909,7 +909,7 @@ 
Now, views are changed to ‘coronal’ and ‘axial’ views with coordinat
 )
-plot demo more plotting
<nilearn.plotting.displays._slicers.YZSlicer object at 0x7fc910aa9610>
+plot demo more plotting
<nilearn.plotting.displays._slicers.YZSlicer object at 0x7fac7be01040>
 

 

 
@@ -927,7 +927,7 @@ 
Visualizing three views in 2x2 fashion)
-plot demo more plotting
<nilearn.plotting.displays._slicers.TiledSlicer object at 0x7fc9110679a0>
+plot demo more plotting
<nilearn.plotting.displays._slicers.TiledSlicer object at 0x7fac7b13a130>
 

 

 
@@ -945,7 +945,7 @@ 
Visualizing three views along multiple rows and columns)
-plot demo more plotting
<nilearn.plotting.displays._slicers.MosaicSlicer object at 0x7fc91113c340>
+plot demo more plotting
<nilearn.plotting.displays._slicers.MosaicSlicer object at 0x7fac7c2b6130>
 

 

 
@@ -962,7 +962,7 @@ 
Now, changing the number of slices along columns)
-plot demo more plotting
<nilearn.plotting.displays._slicers.MosaicSlicer object at 0x7fc91076ea60>
+plot demo more plotting
<nilearn.plotting.displays._slicers.MosaicSlicer object at 0x7fac7c733640>
 

 

 
@@ -979,7 +979,7 @@ 
Now, another way of limiting the number of slices along rows and columns)
-plot demo more plotting
<nilearn.plotting.displays._slicers.MosaicSlicer object at 0x7fc911272c10>
+plot demo more plotting
<nilearn.plotting.displays._slicers.MosaicSlicer object at 0x7fac7c7f5c40>
 

 

 
@@ -1127,7 +1127,7 @@ 
Saving plots to fileplotting.show()
-

Total running time of the script: (0 minutes 27.563 seconds)

+

Total running time of the script: (0 minutes 33.935 seconds)

Estimated memory usage: 916 MB