from scipy.spatial.distance import squareform
from scipy.stats import ttest_1samp
import seaborn as sns
import matplotlib.pyplot as plt
from nltools.stats import (correlation_permutation, one_sample_permutation,
two_sample_permutation, summarize_bootstrap,
matrix_permutation)
from nltools.stats import regress as regression
from nltools.plotting import (plot_stacked_adjacency, plot_silhouette)
from nltools.utils import (all_same, attempt_to_import, concatenate,
_bootstrap_apply_func)
from .design_matrix import Design_Matrix
from joblib import Parallel, delayed
# Optional dependencies
nx = attempt_to_import('networkx', 'nx')
MAX_INT = np.iinfo(np.int32).max
class Adjacency(object):
'''
Adjacency is a class to represent Adjacency matrices as a vector rather
than a 2-dimensional matrix. This makes it easier to perform data
manipulation and analyses.
Args:
data: pandas data instance or list of files
matrix_type: (str) type of matrix. Possible values include:
['distance','similarity','directed','distance_flat',
'similarity_flat','directed_flat']
__author__ = ["Luke Chang"]
__license__ = "MIT"
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from nltools.stats import two_sample_permutation, one_sample_permutation
from nilearn.plotting import plot_glass_brain, plot_stat_map, view_img, view_img_on_surf
from nltools.prefs import MNI_Template, resolve_mni_path
from nltools.utils import attempt_to_import
import warnings
# Optional dependencies
ipywidgets = attempt_to_import('ipywidgets',
name='ipywidgets',
fromlist=['interact', 'fixed', 'widgets'])
def plot_interactive_brain(brain,
threshold=1e-6,
surface=False,
percentile_threshold=False,
anatomical=None,
**kwargs):
"""
This function leverages nilearn's new javascript based brain viewer functions to create interactive plotting functionality.
Args:
brain (nltools.Brain_Data): a Brain_Data instance of 1d or 2d shape (i.e. 3d or 4d volume)
threshold (float/str): threshold to initialize the visualization, maybe be a percentile string; default 0
import matplotlib.pyplot as plt
import numpy as np
from numpy.fft import fft, fftfreq
from nltools.stats import two_sample_permutation, one_sample_permutation
from nilearn.plotting import plot_glass_brain, plot_stat_map, view_img, view_img_on_surf
from nltools.prefs import MNI_Template, resolve_mni_path
from nltools.utils import attempt_to_import
import warnings
import sklearn
import os
# Optional dependencies
ipywidgets = attempt_to_import(
"ipywidgets",
name="ipywidgets",
fromlist=[
"interact", "fixed", "widgets", "BoundedFloatText", "BoundedIntText"
],
)
def plot_interactive_brain(
brain,
threshold=1e-6,
surface=False,
percentile_threshold=False,
anatomical=None,
**kwargs,
):
"""
This function leverages nilearn's new javascript based brain viewer functions to create interactive plotting functionality.
__license__ = "MIT"
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from nltools.stats import two_sample_permutation, one_sample_permutation
from nilearn.plotting import plot_glass_brain, plot_stat_map, view_img, view_img_on_surf
from nltools.prefs import MNI_Template, resolve_mni_path
from nltools.utils import attempt_to_import
import warnings
import os
# Optional dependencies
ipywidgets = attempt_to_import("ipywidgets",
name="ipywidgets",
fromlist=["interact", "fixed", "widgets"])
def plot_interactive_brain(brain,
threshold=1e-6,
surface=False,
percentile_threshold=False,
anatomical=None,
**kwargs):
"""
This function leverages nilearn's new javascript based brain viewer functions to create interactive plotting functionality.
Args:
brain (nltools.Brain_Data): a Brain_Data instance of 1d or 2d shape (i.e. 3d or 4d volume)
threshold (float/str): threshold to initialize the visualization, maybe be a percentile string; default 0