black and codespell for the jupyter book

narratives to the results

content/research_report/results-atlas.md

@@ -11,12 +11,12 @@ kernelspec:
   name: python3
 ---
 
-```{code-cell} ipython3
+```{code-cell}
 :tags: [hide-input, hide-output]
 
 import warnings
 
-warnings.filterwarnings('ignore')
+warnings.filterwarnings("ignore")
 from fmriprep_denoise.dataset.atlas import ATLAS_METADATA
 from fmriprep_denoise.visualization import figures, utils
 
@@ -29,33 +29,200 @@ path_root = utils.get_data_root() / "denoise-metrics"
 # Results: atlas level
 
 It is possible to view the data at atlas level!
+Please click on the launch button to lunch the Binder instance for interactive data viewing.
+
+In the report we used four atlases, three of them came with multiple parcellation schemes.
+
+- Gordon atlas: 333
+- Schaefer 7 network atlas: 100, 200, 300, 400, 500, 600, 800
+- Multiresolution Intrinsic Segmentation Template (MIST): 7, 12, 20, 36, 64, 122, 197, 325, 444, “ROI” (210 parcels, 122 split by the midline)
+- DiFuMo atlas: 64, 128, 256, 512, 1024
+
+## Before we start: Loss of temporal degrees of freedom
+
+As any denoising strategy aims at a particular trade-off between the amount of noise removed and the preservation of degrees of freedom for signals, first and foremost, we would like to presentthe loss of temporal degrees of freedom.
+
+This is an important consideration accompanying the remaining metrics.
+
+```{code-cell}
+:tags: [hide-input]
+
+from fmriprep_denoise.visualization import degrees_of_freedom_loss
+
+
+def notebook_plot_loss_degrees_of_freedom(criteria_name, fmriprep_version):
+    datasets = ["ds000228", "ds000030"]
+    data = degrees_of_freedom_loss.load_data(
+        path_root, datasets, criteria_name, fmriprep_version
+    )
+    degrees_of_freedom_loss.plot_stats(data)
+
+
+criteria_name = widgets.Dropdown(
+    options=["stringent", "minimal", None],
+    value="stringent",
+    description="Threshould: ",
+    disabled=False,
+)
+
+fmriprep_version = widgets.Dropdown(
+    options=["fmriprep-20.2.1lts", "fmriprep-20.2.5lts"],
+    value="fmriprep-20.2.1lts",
+    description="Preporcessing version : ",
+    disabled=False,
+)
+
+interactive(
+    notebook_plot_loss_degrees_of_freedom,
+    criteria_name=criteria_name,
+    fmriprep_version=fmriprep_version,
+)
+```
+
+## Each parcelation scheme
+
+We can also plot them by each parcellation schemes.
+
+This is the original way Ciric and colleagues (2017) presented their results!
+
+### Gordon atlas
+
+```{code-cell}
+atlas = "gordon333"
 
-```{code-cell} ipython3
 dataset = widgets.Dropdown(
-    options=['ds000228', 'ds000030'],
-    description='Dataset : ',
-    disabled=False
+    options=["ds000228", "ds000030"], description="Dataset : ", disabled=False
 )
 fmriprep_version = widgets.Dropdown(
-    options=['fmriprep-20.2.1lts', 'fmriprep-20.2.5lts'],
-    description='fmriprep : ',
-    disabled=False
+    options=["fmriprep-20.2.1lts", "fmriprep-20.2.5lts"],
+    description="fmriprep : ",
+    disabled=False,
 )
-atlas = widgets.Dropdown(
-    options=ATLAS_METADATA.keys(),
-    description='atlas : ',
-    disabled=False
+dimension = widgets.Dropdown(
+    description="dimensions : ",
+    options=ATLAS_METADATA[atlas]["dimensions"],
+    disabled=False,
+)
+
+
+@interact(ds=dataset, f=fmriprep_version, d=dimension)
+def show_atlas(ds, f, d):
+    print(ds, f, ATLAS_METADATA[atlas]["atlas"], "dimensions: ", d)
+    figures.plot_motion_resid(ds, f, path_root, atlas_name=atlas, dimension=d)
+    figures.plot_distance_dependence(ds, f, path_root, atlas_name=atlas, dimension=d)
+    figures.plot_network_modularity(ds, f, path_root, atlas_name=atlas, dimension=d)
+```
+
+### MIST
+
+```{code-cell}
+atlas = "mist"
+
+dataset = widgets.Dropdown(
+    options=["ds000228", "ds000030"], description="Dataset : ", disabled=False
+)
+fmriprep_version = widgets.Dropdown(
+    options=["fmriprep-20.2.1lts", "fmriprep-20.2.5lts"],
+    description="fmriprep : ",
+    disabled=False,
 )
 dimension = widgets.Dropdown(
-        description='dimensions : ',
-        disabled=False
-    )
+    description="dimensions : ",
+    options=ATLAS_METADATA[atlas]["dimensions"],
+    disabled=False,
+)
+
+
+@interact(ds=dataset, f=fmriprep_version, d=dimension)
+def show_atlas(ds, f, d):
+    print(ds, f, ATLAS_METADATA[atlas]["atlas"], "dimensions: ", d)
+    figures.plot_motion_resid(ds, f, path_root, atlas_name=atlas, dimension=d)
+    figures.plot_distance_dependence(ds, f, path_root, atlas_name=atlas, dimension=d)
+    figures.plot_network_modularity(ds, f, path_root, atlas_name=atlas, dimension=d)
+```
+
+### Schaefer 7 network
+
+```{code-cell}
+atlas = "schaefer7networks"
+
+dataset = widgets.Dropdown(
+    options=["ds000228", "ds000030"], description="Dataset : ", disabled=False
+)
+fmriprep_version = widgets.Dropdown(
+    options=["fmriprep-20.2.1lts", "fmriprep-20.2.5lts"],
+    description="fmriprep : ",
+    disabled=False,
+)
+dimension = widgets.Dropdown(
+    description="dimensions : ",
+    options=ATLAS_METADATA[atlas]["dimensions"],
+    disabled=False,
+)
+
+
+@interact(ds=dataset, f=fmriprep_version, d=dimension)
+def show_atlas(ds, f, d):
+    print(ds, f, ATLAS_METADATA[atlas]["atlas"], "dimensions: ", d)
+    figures.plot_motion_resid(ds, f, path_root, atlas_name=atlas, dimension=d)
+    figures.plot_distance_dependence(ds, f, path_root, atlas_name=atlas, dimension=d)
+    figures.plot_network_modularity(ds, f, path_root, atlas_name=atlas, dimension=d)
+```
+
+### DiFuMo
+
+```{code-cell}
+atlas = "difumo"
+
+dataset = widgets.Dropdown(
+    options=["ds000228", "ds000030"], description="Dataset : ", disabled=False
+)
+fmriprep_version = widgets.Dropdown(
+    options=["fmriprep-20.2.1lts", "fmriprep-20.2.5lts"],
+    description="fmriprep : ",
+    disabled=False,
+)
+dimension = widgets.Dropdown(
+    description="dimensions : ",
+    options=ATLAS_METADATA[atlas]["dimensions"],
+    disabled=False,
+)
+
+
+@interact(ds=dataset, f=fmriprep_version, d=dimension)
+def show_atlas(ds, f, d):
+    print(ds, f, ATLAS_METADATA[atlas]["atlas"], "dimensions: ", d)
+    figures.plot_motion_resid(ds, f, path_root, atlas_name=atlas, dimension=d)
+    figures.plot_distance_dependence(ds, f, path_root, atlas_name=atlas, dimension=d)
+    figures.plot_network_modularity(ds, f, path_root, atlas_name=atlas, dimension=d)
+```
+
+## View as atlas collection
+
+You can view the metrics by atlas collections here.
+
+We will summmarise the metrics by each atlas collection.
+
+**The summary statistics are computed on the fly, it might take a bit of time.**
+
+```{code-cell}
+dataset = widgets.Dropdown(
+    options=["ds000228", "ds000030"], description="Dataset : ", disabled=False
+)
+fmriprep_version = widgets.Dropdown(
+    options=["fmriprep-20.2.1lts", "fmriprep-20.2.5lts"],
+    description="fmriprep : ",
+    disabled=False,
+)
+atlas = widgets.Dropdown(
+    description="atlas : ", options=list(ATLAS_METADATA.keys()), disabled=False
+)
+
 
-@interact(ds=dataset, f=fmriprep_version, a=atlas, d=dimension)
-def print_city(ds, f, a, d):
-    dimension.options = ATLAS_METADATA[a]['dimensions']
-    print(ds, f, a, d)
-    figures.plot_motion_resid(ds, f, path_root, atlas_name=a, dimension=d)
-    figures.plot_distance_dependence(ds, f, path_root, atlas_name=a, dimension=d)
-    figures.plot_network_modularity(ds, f, path_root, atlas_name=a, dimension=d)
+@interact(ds=dataset, f=fmriprep_version, a=atlas)
+def show_atlas(ds, f, a):
+    print(ds, f, a)
+    figures.plot_motion_resid(ds, f, path_root, atlas_name=a, dimension=None)
+    figures.plot_distance_dependence(ds, f, path_root, atlas_name=a, dimension=None)
+    figures.plot_network_modularity(ds, f, path_root, atlas_name=a, dimension=None)
 ```