Quentin Bertrand QB3

## uot.py
import numpy as np
import skglm
import celer
import sklearn.linear_model
import scipy.sparse as sp


def get_X_lasso(n, m):
    """ Construction of design matrix H

## discrete_cmap.py
# By Jake VanderPlas
# License: BSD-style

import matplotlib.pyplot as plt
import numpy as np


def discrete_cmap(N, base_cmap=None):
    """Create an N-bin discrete colormap from the specified input map"""

## fail_reweighted_right_audi.py
"""
================================================================
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
================================================================

Runs an (ir)MxNE (L1/L2 [1]_ or L0.5/L2 [2]_ mixed norm) inverse solver.
L0.5/L2 is done with irMxNE which allows for sparser
source estimates with less amplitude bias due to the non-convexity
of the L0.5/L2 mixed norm penalty.

## bench_mixed_norm.py
"""
================================================================
Benchmark pure python vs blas Block Coordinate Descent
================================================================

References
----------
.. [1] Gramfort A., Kowalski M. and Hamalainen, M.
   "Mixed-norm estimates for the M/EEG inverse problem using accelerated
   gradient methods", Physics in Medicine and Biology, 2012.

## plot_simulated_evoked_data.py
"""
==============================
Generate simulated evoked data
==============================

"""
# Author: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#         Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

## plot_camcan_example.py
# -*- coding: utf-8 -*-
"""
========================
cam-CAN tutorial dataset
========================
"""
	import numpy as np
	import skglm
	import celer
	import sklearn.linear_model
	import scipy.sparse as sp


	def get_X_lasso(n, m):
	""" Construction of design matrix H
	# By Jake VanderPlas
	# License: BSD-style

	import matplotlib.pyplot as plt
	import numpy as np


	def discrete_cmap(N, base_cmap=None):
	"""Create an N-bin discrete colormap from the specified input map"""
	"""
	================================================================
	Compute sparse inverse solution with mixed norm: MxNE and irMxNE
	================================================================

	Runs an (ir)MxNE (L1/L2 [1]_ or L0.5/L2 [2]_ mixed norm) inverse solver.
	L0.5/L2 is done with irMxNE which allows for sparser
	source estimates with less amplitude bias due to the non-convexity
	of the L0.5/L2 mixed norm penalty.
	"""
	================================================================
	Benchmark pure python vs blas Block Coordinate Descent
	================================================================

	References
	----------
	.. [1] Gramfort A., Kowalski M. and Hamalainen, M.
	"Mixed-norm estimates for the M/EEG inverse problem using accelerated
	gradient methods", Physics in Medicine and Biology, 2012.
	"""
	==============================
	Generate simulated evoked data
	==============================

	"""
	# Author: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
	# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
	#
	# License: BSD (3-clause)
	# -- coding: utf-8 --
	"""
	========================
	cam-CAN tutorial dataset
	========================
	"""