Figures (#105)

* Fixed missing caps in kCSD

* Removed redundant functions

* Renamed script

* Updated readme for the figures

* Added separate script for figure 4

* Set relative paths to figures folder

Author: m-kowalska

figures/kCSD_properties/README.txt

@@ -9,17 +9,16 @@ sources_electrodes.odg
 Figure 2 - 1D spectral properties of kCSD method
 
 figure_eigensources_M_1D.py
-figure_eigensources_R_1D.py (another version with inset plot)
 
 ~~~~~~~~~~~~~~~~~~~~~~~
-Figure 3 and 4 - Noise-free Electrode/Basis source placement
+Figure 3 and 4 - Noise-free Electrode / Basis source placement
 
 figure_Tbasis.py
 
 ~~~~~~~~~~~~~~~~~~~~~~~
 Figure 3 and 4 - Noisy electrodes / Basis source placement
 
-figure_Tbasis.py
+figure_Tbasis_noise.py
 
 ~~~~~~~~~~~~~~~~~~~~~~~
 Figure 5 - L-curve method for regularization
@@ -46,14 +45,19 @@ error_maps_2D/point_error_small_100_all_ele.npy
 ~~~~~~~~~~~~~~~~~~~~~~~
 Figure 9 - Reliability map; Use case in a 2D dipolar large source
 
-figure7_kCSD2d.py
+kCSD_with_reliability_map_2D.py
 
 error_maps_2D/point_error_large_100_all_ele.npy
 error_maps_2D/point_error_small_100_all_ele.npy
 
 ~~~~~~~~~~~~~~~~~~~~~~~
 Figure 10 - Average Error (Diff) when broken electrode and loss in reconstruction quality
 
+You will need to run tutorial3.py first or download files from here
+https://www.dropbox.com/sh/6kykj4d3dx3fp5s/AAACtN49VCbAHA9otOfNXbnOa?dl=0
+
+tutorial_broken_electrodes_diff_err.py
+
 ~~~~~~~~~~~~~~~~~~~~~~~
 Figure 11 - Basic features tutorial
 
@@ -75,7 +79,6 @@ https://www.dropbox.com/sh/6kykj4d3dx3fp5s/AAACtN49VCbAHA9otOfNXbnOa?dl=0
 (generated from tweaking tutorial3.py)
 
 tutorial_broken_electrodes.py
-tutorial_broken_electrodes_sig_err.py (another variant)
 
 ~~~~~~~~~~~~~~~~~~~~~~~~
 Figure 14 - L-Curve and CV landscape

figures/kCSD_properties/figure_Tbasis.py

@@ -3,13 +3,10 @@
 """
 
 import os
-from os.path import expanduser
 import numpy as np
 from figure_properties import *
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
-import datetime
-import time
 
 import targeted_basis as tb
 
@@ -122,7 +119,7 @@ def make_subplot(ax, true_csd, est_csd, estm_x, title=None, ele_pos=None,
     return ax
 
 
-def generate_figure(R, MU, n_src, true_csd_xlims, total_ele, save_path,
+def generate_figure(R, MU, n_src, true_csd_xlims, total_ele,
                     method='cross-validation', Rs=None, lambdas=None,
                     noise=0):
     """
@@ -284,232 +281,21 @@ def generate_figure(R, MU, n_src, true_csd_xlims, total_ele, save_path,
     handles, labels = ax.get_legend_handles_labels()
     fig.legend(handles, labels, loc='lower center', ncol=3, frameon=False)
 
-    fig.savefig(os.path.join(save_path, 'targeted_basis_' + method +
-                             '_noise_' + str(noise) + '.png'), dpi=300)
-    plt.show()
-
-
-def generate_figure_CVLC(R, MU, n_src, true_csd_xlims, total_ele, save_path,
-                         Rs=None, lambdas=None, noise=0):
-    """
-    Generates figure for targeted basis investigation including results from
-    both cross-validation and L-curve.
-
-    Parameters
-    ----------
-    R: float
-        Thickness of the groundtruth source.
-        Default: 0.2.
-    MU: float
-        Central position of Gaussian source
-        Default: 0.25.
-    nr_src: int
-        Number of basis sources.
-    true_csd_xlims: list
-        Boundaries for ground truth space.
-    total_ele: int
-        Number of electrodes.
-    save_path: string
-        Directory.
-    Rs: numpy 1D array
-        Basis source parameter for crossvalidation.
-        Default: None.
-    lambdas: numpy 1D array
-        Regularization parameter for crossvalidation.
-        Default: None.
-    noise: float
-        Determines the level of noise in the data.
-        Default: 0.
-
-    Returns
-    -------
-    None
-    """
-
-    m_cv = 'cross-validation'
-    m_lc = 'L-curve'
-    method = 'CV_LC'
-    ele_lims = [0, 1.]
-    csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
-                                                            true_csd_xlims, R,
-                                                            MU, total_ele,
-                                                            ele_lims,
-                                                            noise=noise)
-
-    fig = plt.figure(figsize=(15, 12))
-    widths = [1, 1, 1]
-    heights = [1, 1, 1]
-    gs = gridspec.GridSpec(3, 3, height_ratios=heights, width_ratios=widths,
-                           hspace=0.45, wspace=0.3)
-
-    ax = fig.add_subplot(gs[0, 0])
-    xmin = 0
-    xmax = 1
-    ext_x = 0
-    obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
-    obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
-    make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
-                 ele_pos=ele_pos, title='Basis limits = [0, 1]', xlabel=False,
-                 ylabel=True, letter='A', est_csd_LC=obj_LC.values('CSD'))
-
-    ax = fig.add_subplot(gs[0, 1])
-    xmin = -0.5
-    xmax = 1
-    ext_x = -0.5
-    obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
-    obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
-    make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
-                 ele_pos=ele_pos, title='Basis limits = [0, 0.5]',
-                 xlabel=False, ylabel=False, letter='B',
-                 est_csd_LC=obj_LC.values('CSD'))
-
-    ax = fig.add_subplot(gs[0, 2])
-    xmin = 0
-    xmax = 1.5
-    ext_x = -0.5
-    obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
-    obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
-    make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
-                 ele_pos=ele_pos, title='Basis limits = [0.5, 1]',
-                 xlabel=False, ylabel=False, letter='C',
-                 est_csd_LC=obj_LC.values('CSD'))
-
-    ele_lims = [0, 0.5]
-#    total_ele = 6
-    csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
-                                                            true_csd_xlims, R,
-                                                            MU, total_ele,
-                                                            ele_lims,
-                                                            noise=noise)
-    ax = fig.add_subplot(gs[1, 0])
-    xmin = 0
-    xmax = 1
-    ext_x = 0
-    obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
-    obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
-    make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
-                 ele_pos=ele_pos, title=None, xlabel=False, ylabel=True,
-                 letter='D', est_csd_LC=obj_LC.values('CSD'))
-
-    ax = fig.add_subplot(gs[1, 1])
-    xmin = -0.5
-    xmax = 1
-    ext_x = -0.5
-    obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
-    obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
-    make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
-                 ele_pos=ele_pos, title=None, xlabel=False, ylabel=False,
-                 letter='E', est_csd_LC=obj_LC.values('CSD'))
-
-    ax = fig.add_subplot(gs[1, 2])
-    xmin = 0
-    xmax = 1.5
-    ext_x = -0.5
-    obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
-    obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
-    make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
-                 ele_pos=ele_pos, title=None, xlabel=False, ylabel=False,
-                 letter='F', est_csd_LC=obj_LC.values('CSD'))
-
-    ele_lims = [0.5, 1.]
-    csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
-                                                            true_csd_xlims, R,
-                                                            MU, total_ele,
-                                                            ele_lims,
-                                                            noise=noise)
-    ax = fig.add_subplot(gs[2, 0])
-    xmin = 0
-    xmax = 1
-    ext_x = 0
-    obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
-    obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
-    make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
-                 ele_pos=ele_pos, title=None, xlabel=True, ylabel=True,
-                 letter='G', est_csd_LC=obj_LC.values('CSD'))
-
-    ax = fig.add_subplot(gs[2, 1])
-    xmin = -0.5
-    xmax = 1
-    ext_x = -0.5
-    obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
-    obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
-    make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
-                 ele_pos=ele_pos, title=None, xlabel=True, ylabel=False,
-                 letter='H', est_csd_LC=obj_LC.values('CSD'))
-
-    ax = fig.add_subplot(gs[2, 2])
-    xmin = 0
-    xmax = 1.5
-    ext_x = -0.5
-    obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
-    obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
-                               sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
-                               xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
-    ax = make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
-                      ele_pos=ele_pos, title=None, xlabel=True, ylabel=False,
-                      letter='I', est_csd_LC=obj_LC.values('CSD'))
-    handles, labels = ax.get_legend_handles_labels()
-    fig.legend(handles, labels, loc='lower center', ncol=4, frameon=False)
-
-    fig.savefig(os.path.join(save_path, 'targeted_basis_' + method +
+    fig.savefig(os.path.join('targeted_basis_' + method +
                              '_noise_' + str(noise) + '.png'), dpi=300)
     plt.show()
 
 
 if __name__ == '__main__':
-    home = expanduser('~')
-    DAY = datetime.datetime.now()
-    DAY = DAY.strftime('%Y%m%d')
-    TIMESTR = time.strftime("%H%M%S")
-    SAVE_PATH = home + "/kCSD_results/" + DAY + '/' + TIMESTR
-    tb.makemydir(SAVE_PATH)
-    tb.save_source_code(SAVE_PATH, time.strftime("%Y%m%d-%H%M%S"))
-
     N_SRC = 64
     TRUE_CSD_XLIMS = [0., 1.]
     TOTAL_ELE = 12
     R = 0.2
     MU = 0.25
     method = 'cross-validation'  # L-curve
 #    method = 'L-curve'
-#    Rs = np.arange(0.1, 0.4, 0.05)
     Rs = np.array([0.2])
     lambdas = np.zeros(1)
-    generate_figure(R, MU, N_SRC, TRUE_CSD_XLIMS, TOTAL_ELE, SAVE_PATH,
+    generate_figure(R, MU, N_SRC, TRUE_CSD_XLIMS, TOTAL_ELE,
                     method=method, Rs=Rs, lambdas=lambdas, noise=0)
-#    generate_figure_CVLC(R, MU, N_SRC, TRUE_CSD_XLIMS, TOTAL_ELE, SAVE_PATH,
-#                         Rs=Rs, lambdas=None, noise=10)
+