def plot_cluster_freqs_on_brain(self, cluster_name, xyz, colormap='viridis', vmin=None, vmax=None, do_3d=False):
"""
Plot the frequencies of single electrode cluster on either a 2d or interactive 2d brain.
Parameters
----------
cluster_name: str
Name of column in self.res['clusters']
xyz: np.ndarray
3 x n array of electrode locations. Should be in MNI space.
colormap: str
matplotlib colormap name
vmin: float
lower limit of colormap values. If not given, lowest value in frequency column will be used
vmax: float
upper limit of colormap values. If not given, highest value in frequency column will be used
do_3d:
Whether to plot an interactive 3d brain, or a 2d brain
Returns
-------
If 2d, returns the matplotlib figure. If 3d, returns the html used to render the brain.
"""
# get frequecies for this cluster
freqs = self.res['clusters'][cluster_name].values
# get color for each frequency. Start with all black
colors = np.stack([[0., 0., 0., 0.]] * len(freqs))
# fill in colors of electrodes with defined frequencies
cm = plt.get_cmap(colormap)
cNorm = clrs.Normalize(vmin=np.nanmin(freqs) if vmin is None else vmin,
vmax=np.nanmax(freqs) if vmax is None else vmax)
colors[~np.isnan(freqs)] = cmx.ScalarMappable(norm=cNorm, cmap=cm).to_rgba(freqs[~np.isnan(freqs)])
# if plotting 2d, use the nilearn glass brain
if not do_3d:
fig, ax = plt.subplots()
ni_plot.plot_connectome(np.eye(xyz.shape[0]), xyz,
node_kwargs={'alpha': 0.7, 'edgecolors': None},
node_size=60, node_color=colors, display_mode='lzr',
axes=ax)
divider = make_axes_locatable(ax)
cax = divider.append_axes('bottom', size='4%', pad=0)
cb1 = mpl.colorbar.ColorbarBase(cax, cmap=colormap,
norm=cNorm,
orientation='horizontal')
cb1.set_label('Frequency', fontsize=20)
cb1.ax.tick_params(labelsize=14)
fig.set_size_inches(15, 10)
return fig
# if plotting 3d use the nilearn 3d brain. Unfortunately this doesn't add a colorbar.
else:
# you need to return the html. If in jupyter, it will automatically render
return ni_plot.view_markers(xyz, colors=colors, marker_size=6)
def plot_cluster_freqs_on_brain(self, cluster_name, xyz, colormap='viridis', vmin=None, vmax=None, do_3d=False):
"""
Plot the frequencies of single electrode cluster on either a 2d or interactive 2d brain.
Parameters
----------
cluster_name: str
Name of column in self.res['clusters']
xyz: np.ndarray
3 x n array of electrode locations. Should be in MNI space.
colormap: str
matplotlib colormap name
vmin: float
lower limit of colormap values. If not given, lowest value in frequency column will be used
vmax: float
upper limit of colormap values. If not given, highest value in frequency column will be used
do_3d:
Whether to plot an interactive 3d brain, or a 2d brain
Returns
-------
If 2d, returns the matplotlib figure. If 3d, returns the html used to render the brain.
"""
# get frequecies for this cluster
freqs = self.res['clusters'][cluster_name].values
# get color for each frequency. Start with all black
colors = np.stack([[0., 0., 0., 0.]] * len(freqs))
# fill in colors of electrodes with defined frequencies
cm = plt.get_cmap(colormap)
cNorm = clrs.Normalize(vmin=np.nanmin(freqs) if vmin is None else vmin,
vmax=np.nanmax(freqs) if vmax is None else vmax)
colors[~np.isnan(freqs)] = cmx.ScalarMappable(norm=cNorm, cmap=cm).to_rgba(freqs[~np.isnan(freqs)])
# if plotting 2d, use the nilearn glass brain
if not do_3d:
fig, ax = plt.subplots()
ni_plot.plot_connectome(np.eye(xyz.shape[0]), xyz,
node_kwargs={'alpha': 0.7, 'edgecolors': None},
node_size=60, node_color=colors, display_mode='lzr',
axes=ax)
divider = make_axes_locatable(ax)
cax = divider.append_axes('bottom', size='4%', pad=0)
cb1 = mpl.colorbar.ColorbarBase(cax, cmap=colormap,
norm=cNorm,
orientation='horizontal')
cb1.set_label('Frequency', fontsize=20)
cb1.ax.tick_params(labelsize=14)
fig.set_size_inches(15, 10)
return fig
# if plotting 3d use the nilearn 3d brain. Unfortunately this doesn't add a colorbar.
else:
# you need to return the html. If in jupyter, it will automatically render
return ni_plot.view_markers(xyz, colors=colors, marker_size=6)
def view_markers_with_nilearn(G, node_size=5., node_colouring='black'):
"""
Plot nodes of a BrainNetwork using
:func:`nilearn.plotting.view_connectome()` tool.
Parameters
----------
G : :class:`networkx.Graph`
G should have nodal locations in MNI space indexed by nodal
attribute "centroids"
node_colouring : str or list of str, default 'black'
node_colouring will determine the colour given to each node.
If a single string is given, this string will be interpreted as a
a colour, and all nodes will be rendered in this colour.
If a list of colours is given,
"""
a, node_coords, colour_list, z = graph_to_nilearn_array(G, )
if isinstance(node_colouring, str):
colours = [node_colouring for i in range(len(node_coords))]
elif colour_list is not None:
colours = np.array([node_colouring(x) for x in colour_list])
return plotting.view_markers(node_coords,
colors=colours,
marker_size=node_size)
scans = reduce_mean(scans)
voxel_ids = select_most_varied_voxels(scans)[0:1000]
varied_voxels = [mni_mapping[v] for v in voxel_ids]
regions = [roi_mapping[v] for v in voxel_ids]
all_varied_voxels.extend(varied_voxels)
color = colors[subject-1]
color_data.extend([color for _ in range(len(varied_voxels))])
print(len(all_varied_voxels), len(color_data))
# Open plot in browser
count_map = {}
for i in regions:
count_map[str(i)] = count_map.get(str(i), 0) + 1
print(count_map)
view = plotting.view_markers(all_varied_voxels, color_data, marker_size=3)
view.open_in_browser()
# ---- KAPLAN DATA -----
# This dataset is described in Dehghani et al. 2017: https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.23814
# I received it from Jonas Kaplan, but I am not allowed to share it.
# print("\n\n Stories Data")
# kaplan_reader = StoryDataReader(data_dir=data_dir + "Kaplan_data/")
# kaplan_data = kaplan_reader.read_all_events(subject_ids=[29],language="english")
# sum = 0
#
from networkToIndexDicGordon import dic
import pandas as pd
import argparse
from nilearn import plotting
import numpy as np
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--dic_excel',
required=True,
type=str,
help='path to dictionary excel file')
args = parser.parse_args()
fd = pd.read_excel(args.dic_excel)
for net, indexes in dic.items():
#Checks for every network if all the parcels exists
fd_net = fd[fd["Community"] == net]
parcels = list(fd_net["ParcelID"])
parcels = list(np.array(parcels) - np.array([1] * len(parcels)))
if (indexes != parcels):
print("Parcelation error in ", net)
#Plots all the coordinates - you can compare later with the output from the code
coords = fd_net["Centroid (MNI)"]
coords_new = []
for coord_str in coords:
coords_new.append([float(x) for x in coord_str.split()])
view = plotting.view_markers(coords_new, marker_size=10)
html_name = "test_out/network_plot" + str(net) + ".html"
view.save_as_html(html_name)
def view_nodes_3d(G,
node_size=5.,
node_color='black',
measure=None,
cmap_name=None,
sns_palette=None,
continuous=False,
vmin=None,
vmax=None):
"""
Plot nodes of a BrainNetwork using
:func:`nilearn.plotting.view_markers()` tool.
Insert a 3d plot of markers in a brain into an HTML page.
Parameters
----------
G : :class:`networkx.Graph`
G should have nodal locations in MNI space indexed by nodal
attribute "centroids"
node_size : float or array-like, optional (default=5.)
Size of the nodes showing the seeds in pixels.
node_color : str or list of str (default 'black')
node_colour determines the colour given to each node.
If a single string is given, this string will be interpreted as a
a colour, and all nodes will be rendered in this colour.
If a list of colours is given, it must be the same length as the length
of nodes coordinates.
measure: str, (optional, default=None)
The name of a nodal measure.
cmap_name : Matplotlib colormap
Colormap for mapping intensities of nodes (default=None).
sns_palette: seaborn palette, (optional, default=None)
Discrete color palette only for discrete data. List of colors defining
a color palette (list of RGB tuples from seaborn color palettes).
continuous: bool, (optional, default=False)
Indicate whether the data values are discrete (False) or
continuous (True).
vmin : scalar or None, optional
The minimum value used in colormapping *data*. If *None* the minimum
value in *data* is used.
vmax : scalar or None, optional
The maximum value used in colormapping *data*. If *None* the maximum
value in *data* is used.
Returns
-------
ConnectomeView : plot of the nodes.
It can be saved as an html page or rendered (transparently) by the
Jupyter notebook. Useful methods are :
- 'resize' to resize the plot displayed in a Jupyter notebook
- 'save_as_html' to save the plot to a file
- 'open_in_browser' to save the plot and open it in a web browser.
"""
# get the nodes coordinates
adj_matrix, node_coords = graph_to_nilearn_array(G)
# apply color to all nodes in Graph if node_color is string
if isinstance(node_color, str):
node_color = [node_color for _ in range(len(node_coords))]
# report the attributes of each node in BrainNetwork Graph
nodal_measures = G.report_nodal_measures()
# get the color for each node based on the nodal measure
if measure:
if measure in nodal_measures.columns:
node_color = setup_color_list(df=nodal_measures,
measure=measure,
cmap_name=cmap_name,
sns_palette=sns_palette,
continuous=continuous,
vmin=vmin,
vmax=vmax)
else:
warnings.warn(
"Measure \"{}\" does not exist in nodal attributes of graph. "
"The default color will be used for all nodes.".format(
measure))
node_color = [node_color for _ in range(len(node_coords))]
# plot nodes
ConnectomeView = plotting.view_markers(node_coords,
marker_color=node_color,
marker_size=node_size)
return ConnectomeView
#%%
from nilearn import plotting
ofc_electrodes = [(-3.17,37.28,7.56),(8.67,38.04,-15.47),(-4.26,38.19,-12.07),(7.67,38.07,-15.91),(-1.98,40.24,-15.36),(7.08,32.13,-14.81),(-8.06,36.04,-6.05),(10.87,35.06,-5.09),(-1.03,36.31,-10.91),(13.66,37.28,-17.09),(-7.02,36.94,-13.11),(4.18,38.13,-12.05),(-0.97,35.20,-10.35),(10.56,31.60,-5.53),(-8.95,43.22,-13.4),(10.63,39.65,-13.44),(-8.97,34.15,-12.16),(27.02,31.69,-5.48),(-8.64,36.74,-6.98),(6.32,35.04,-11.7),(-3.53,36.85,-16.95),(3.94,31.75,-14.51),(0.87,42.45,-1.65),(15.93,33.66,-0.4)]
view = plotting.view_markers(ofc_electrodes, marker_size=10)
view.save_as_html('ueli_ofc_electrodes.html')
# %%
#load transform from subj to template
sub2template= np.loadtxt(snakemake.input.xfm_ras)
#plot electrodes transformed (affine) to MNI space, with MNI glass brain
from nilearn import plotting
coords = df[['x','y','z']].to_numpy()
#print(coords.shape)
#to plot in mni space, need to transform coords
tcoords = np.zeros(coords.shape)
for i in range(len(coords)):
vec = np.hstack([coords[i,:],1])
tvec = np.linalg.inv(sub2template) @ vec.T
tcoords[i,:] = tvec[:3]
html_view = plotting.view_markers(tcoords)
html_view.save_as_html(snakemake.output.html)
#plot subject native space electrodes with glass brain
adjacency_matrix = np.zeros([len(coords),len(coords)])
display = plotting.plot_connectome(adjacency_matrix, tcoords)
display.savefig(snakemake.output.png)
display.close()
scans.extend([event.scan for event in block.scan_events])
scans = reduce_mean(scans)
voxel_ids = select_most_varied_voxels(scans)[0:1000]
varied_voxels = [mni_mapping[v] for v in voxel_ids]
regions = [roi_mapping[v] for v in voxel_ids]
all_varied_voxels.extend(varied_voxels)
color = colors[subject - 1]
color_data.extend([color for _ in range(len(varied_voxels))])
print(len(all_varied_voxels), len(color_data))
# Open plot in browser
count_map = {}
for i in regions:
count_map[str(i)] = count_map.get(str(i), 0) + 1
print(count_map)
view = plotting.view_markers(all_varied_voxels, color_data, marker_size=3)
view.open_in_browser()
# ---- KAPLAN DATA -----
# This dataset is described in Dehghani et al. 2017: https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.23814
# I received it from Jonas Kaplan, but I am not allowed to share it.
# print("\n\n Stories Data")
# kaplan_reader = StoryDataReader(data_dir=data_dir + "Kaplan_data/")
# kaplan_data = kaplan_reader.read_all_events(subject_ids=[29],language="english")
# sum = 0
#
# for subject_id in kaplan_data.keys():
# all_scans = []
# for block in kaplan_data[subject_id]:
# # These are all already sorted, so I think you don't even need timesteps.
from nilearn import plotting
import pandas as pd
import scipy.io as sio
import numpy as np
# Load subjVar as pandas dataframe
fname = '/Users/pinheirochagas/Desktop/elinfo.csv'
mni = np.loadtxt(fname, delimiter=',')
fname = '/Users/pinheirochagas/Desktop/colors.csv'
colors = np.loadtxt(fname, delimiter=',')
view = plotting.view_markers(mni, colors, marker_size=5)
view.open_in_browser()
view.save_as_html("/Users/pinheirochagas/Desktop/elinfo.html")
img = datasets.fetch_localizer_button_task()
view = plotting.view_img_on_surf(img, threshold='90%', surf_mesh='fsaverage')
fname = '/Users/pinheirochagas/Downloads/arithmetic_association-test_z_FDR_0.01.nii'
plotting.plot_glass_brain(fname, display_mode='lyrz', threshold=3)
plt.savefig('/Users/pinheirochagas/Desktop/arith_neurosynth.png')
plotting.plot_glass_brain(stat_img, threshold=3)
from nilearn import plotting, datasets, surface
dmn_coords = [(0, -52, 18), (-46, -68, 32), (46, -68, 32), (1, 50, -5)]
img = datasets.fetch_localizer_button_task()['tmap']
view = plotting.view_img_on_surf(fname, threshold='90%', surf_mesh='fsaverage')