L curve related figures

figures/kCSD_properties/fig2_cv_vs_lc.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Mar 30 21:12:08 2018
+
+@author: Wladek
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 30 17:01:35 2018
+
+@author: Wladek
+"""
+import numpy as np
+import pylab as py
+import pandas as pd
+import os
+
+
+py.close('all')
+npDir = "./LCurve/"
+os.chdir(npDir)
+
+nss = 10
+rms_lc = np.zeros((nss,19))
+lam_lc = np.zeros((nss,19))
+rms_cv = np.zeros((nss,19))
+lam_cv = np.zeros((nss,19))
+for i in range(nss):
+    #os.chdir(npDir+'example_figs/lc_all/lc'+str(i)+'/')
+    os.chdir(npDir+'/lc'+str(i)+'/')
+    dip = pd.read_excel('lc' + str(i) +'_.xlsx')
+    rms_lc[i] = np.array([dip['RMS'].values])
+    lam_lc[i] = np.array([dip['Lambda'].values])
+    os.chdir(npDir+'example_figs/cv_all/cv'+str(i)+'/')
+    dip = pd.read_excel('cv' + str(i) +'_.xlsx')
+    rms_cv[i] = np.array([dip['RMS'].values])
+    lam_cv[i] = np.array([dip['Lambda'].values])
+
+fig = py.figure(figsize=(12, 10), dpi=100)
+ax1 = fig.add_subplot(211)
+py.xlabel('',fontsize = 25)
+n_spec = np.linspace(1e-2,1,19)
+
+lw = 0.5
+mn_rms = np.mean(rms_lc, axis=0)
+st_rms = np.std(rms_lc, axis=0)
+py.plot(n_spec, mn_rms, marker = 'o', color = 'blue', label = 'l-curve')
+py.fill_between(n_spec, mn_rms - st_rms, 
+                mn_rms + st_rms, alpha = 0.3, color = 'blue')
+mn_rms = np.mean(rms_cv, axis=0)
+st_rms = np.std(rms_cv, axis=0)
+py.plot(n_spec, mn_rms, marker = 'o', color = 'green', label = 'cross-validation')
+py.fill_between(n_spec, mn_rms - st_rms, 
+                mn_rms + st_rms, alpha = 0.3, color = 'green')
+py.legend(fontsize = 15)
+#py.xlabel('Noise',fontsize = 25)
+py.ylabel('Estimation error',fontsize = 18)
+py.xlabel('Relative noise level',fontsize = 15)
+py.xticks(fontsize = 15)
+py.yticks(fontsize = 15)
+#py.xlim(0, 0.9)
+py.ylim(0, 100)
+
+'''second plot'''
+ax2 = fig.add_subplot(212)
+mn_lam = np.mean(lam_lc, axis=0)
+st_lam = np.std(lam_lc, axis=0)
+py.plot(n_spec, mn_lam, marker = 'o', color = 'blue', label = 'l-curve')
+py.fill_between(n_spec, mn_lam - st_lam,
+                mn_lam + st_lam, alpha = 0.3, color = 'blue')
+mn_lam = np.mean(lam_cv, axis=0)
+st_lam = np.std(lam_cv, axis=0)
+py.plot(n_spec, mn_lam, marker = 'o', color = 'green', label = 'cross-validation')
+py.fill_between(n_spec, mn_lam - st_lam,
+                mn_lam + st_lam, alpha = 0.3, color = 'green')
+py.legend(fontsize = 15, loc =2)
+py.xticks(fontsize = 15)
+py.yticks(fontsize = 15)
+#py.xlim(0, 0.9)
+py.ticklabel_format(style='sci', axis='y',  scilimits=(0,0))
+py.ylabel('Lambda',fontsize = 18)
+py.xlabel('Relative noise level',fontsize = 15)

figures/kCSD_properties/kCSD1D_test_fig1.py

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+'''
+This script is used to test Current Source Density Estimates, 
+using the kCSD method Jan et.al (2012)
+
+This script is in alpha phase.
+
+This was written by :
+Chaitanya Chintaluri, 
+Laboratory of Neuroinformatics,
+Nencki Institute of Exprimental Biology, Warsaw.
+'''
+import os
+import scipy.stats as st
+import numpy as np
+from scipy.integrate import simps 
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+from kcsd import KCSD1D
+from pandas import DataFrame
+
+def do_kcsd(ele_pos, pots, **params):
+    """
+    Function that calls the KCSD1D module
+    """
+    num_ele = len(ele_pos)
+    pots = pots.reshape(num_ele, 1)
+    ele_pos = ele_pos.reshape(num_ele, 1)
+    Lamb = [-9, -3]
+    lambdas = np.logspace(Lamb[0], Lamb[1], 50, base=10)
+    k = KCSD1D(ele_pos, pots, **params)
+    if name == 'lc':
+        k.L_curve(lambdas=lambdas, Rs=ery)
+    else:
+        k.cross_validate(Rs=ery, lambdas=lambdas)
+    est_csd = k.values('CSD')
+    est_pot = k.values('POT')
+    return k, est_csd, est_pot
+
+def make_plots(title, m_norm, m_resi, true_csd, curveseq, ele_y,
+               pots, k_csd_x, k_csd_y, est_pot, est_csd, save_as):
+    """
+    Shows 3 plots
+    1_ true CSD generated based on the random seed given
+    2_ interpolated LFT (NOT kCSD pot though), generated by simpsons rule integration
+    3_ results from the kCSD 2D for the default values
+    """
+    #True CSD
+    fig = plt.figure(figsize=(16, 12), dpi=120)
+    xrange = np.linspace(0, 1, len(true_csd))
+    ax1 = plt.subplot(221)
+    ax1.plot(ele_y, pots*1e3, 'r', marker='o', linewidth=0, label='measured potential')
+    ax1.scatter(ele_y, np.zeros(len(ele_y)), 8, color='black')
+    ax1.plot(xrange, est_pot*1e3, label='recon. potential', color='blue')
+    ax1.set_ylabel('Potential [mV]', fontsize=15)
+    ax1.set_xlabel('Distance', fontsize=15)
+    plt.legend()
+
+    ax2 = plt.subplot(222)
+    plt.plot(xrange, true_csd, label='true csd', color='red')
+    plt.plot(xrange, est_csd, label='recon. csd', color='blue')
+    plt.scatter(ele_y, np.zeros(len(ele_y)), 8, color='black')
+    ax2.set_ylabel('CSD [mA/mm]', fontsize=15)
+    ax2.set_xlabel('Distance', fontsize=15)
+    plt.legend(loc=1)
+    Lamb = [-9, -3]
+    lambdas = np.logspace(Lamb[0], Lamb[1], 50, base=10)
+    imax = np.argmax(curveseq[np.argmax(np.max(curveseq, axis=-1))])
+
+    ax_L = fig.add_subplot(223)
+    plt.ylabel("Norm of the model", fontsize=15)
+    plt.xlabel("Norm of the prediction error", fontsize=15)
+    ax_L.plot(m_resi, m_norm, marker=".", c="green")
+    #print(m_resi, m_norm)
+    ax_L.plot([m_resi[imax]], [m_norm[imax]], marker="o", c="red")
+    x = [m_resi[0], m_resi[imax], m_resi[-1]]
+    y = [m_norm[0], m_norm[imax], m_norm[-1]]
+    ax_L.fill(x, y, alpha=0.2)
+    # ax_L.set_xscale('log')
+    # ax_L.set_yscale('log')
+
+
+    ax4 = fig.add_subplot(224)
+    plt.imshow(curveseq, extent=[Lamb[0], Lamb[1], ery[-1], ery[0]],
+               interpolation='none', aspect='auto',
+               cmap='BrBG_r', vmax=np.max(curveseq), vmin=-np.max(curveseq))
+    plt.colorbar()
+    ax4.set_ylabel('R parameter', fontsize=15)
+    ax4.set_xlabel('log(Lambda)', fontsize=15)
+#    fig.suptitle("Lambda =" + title[0] + " I R = " + title[1] + 
+#                 " I Noise_lvl =" + title[2])
+    fig.savefig(save_as+'.png')
+
+    return np.linalg.norm(true_csd - est_csd)
+
+def generate_csd_1D(src_width, name, srcs=np.array([0]),
+                    start_x=0., end_x=1.,
+                    start_y=0., end_y=1.,
+                    res_x=50, res_y=50):
+    """
+    Gives CSD profile at the requested spatial location, at 'res' resolution
+    """
+    csd_y = np.linspace(start_x, end_x, res_x)
+    csd_x = np.linspace(start_x, end_x, res_x)
+#    f = prof.gauss_2d_small(csd_x, csd_y, src_width, tpy=name, srcs=srcs)
+    src_peak = np.exp(-((csd_x-0.25)**2)/(2.*src_width))
+    src_thr1 = .5*np.exp(-((csd_x-0.5)**2)/(2.*src_width))
+    src_thr2 = .5*np.exp(-((csd_x-0.6)**2)/(2.*src_width))
+    f = src_peak - src_thr1 - src_thr2
+    csd_y = np.linspace(start_x, end_x, res_x)
+    return csd_x, csd_y, f
+
+def integrate_1D(x0, csd_x, csd, h):
+    m = np.sqrt((csd_x-x0)**2 + h**2) - abs(csd_x-x0)
+    y = csd * m
+    I = simps(y, csd_x)
+    return I
+
+def calculate_potential_1D(csd, measure_locations, csd_space_x, h):
+    sigma = 1
+    print(measure_locations.shape, csd_space_x.shape, csd.shape)
+    pots = np.zeros(len(measure_locations))
+    for ii in range(len(measure_locations)):
+        pots[ii] = integrate_1D(measure_locations[ii], csd_space_x, csd, h)
+    pots *= 1/(2.*sigma) #eq.: 26 from Potworowski et al
+    return pots
+
+def transformAndNormalizeLongGrid(D):
+    #D is a dictionary
+    locations = np.zeros((len(D), 2))
+    for i in range(len(D)):
+        locations[i, 0] = D[i]['x']
+        locations[i, 1] = D[i]['y']
+    return locations
+
+def generate_electrodes(inpos, lpos, b):
+    loc = {}
+    cordx = -(inpos[0] - lpos[0])/b
+    cordy = -(inpos[1] - lpos[1])/b
+    def _addLocToDict(D, chnum, x, y):
+        d = {}
+        d['x'] = x
+        d['y'] = y
+        D[chnum] = d
+    #first add locations just as they suppose to be
+    for i in range(b):
+        _addLocToDict(loc, i, inpos[0] + cordx*i, inpos[1] + cordy*i)
+    loc_mtrx = transformAndNormalizeLongGrid(loc)
+    loc_mtrx = loc_mtrx.T
+    return loc_mtrx[0], loc_mtrx[1]
+
+def electrode_config(ele_res, true_csd, csd_x, csd_y, inpos, lpos):
+    """
+    What is the configuration of electrode positions, between what and what positions
+    """
+    ele_x, ele_y = generate_electrodes(inpos, lpos, ele_res)
+    pots = calculate_potential_1D(true_csd, ele_y.T, csd_y, h=1)
+    ele_pos = np.vstack((ele_x, ele_y)).T     #Electrode configs
+    return ele_pos, pots
+
+def main_loop(src_width, total_ele, inpos, lpos, nm, noise=0, srcs=1):
+    """
+    Loop that decides the random number seed for the CSD profile,
+    electrode configurations and etc.
+    """
+    #TrueCSD
+    t_csd_x, t_csd_y, true_csd = generate_csd_1D(src_width, nm, srcs=srcs,
+                                                 start_x=0, end_x=1.,
+                                                 start_y=0, end_y=1,
+                                                 res_x=100, res_y=100)
+    if type(noise) == float:
+        n_spec = [noise]
+    else:
+        n_spec = noise
+    RMS_wek = np.zeros(len(n_spec))
+    LandR = np.zeros((2, len(n_spec)))
+    for i, noise in enumerate(n_spec):
+        plt.close('all')
+        noise = np.round(noise, 5)
+        print('numer rekonstrukcji: ', i, 'noise level: ', noise)
+        #Electrodes
+        ele_pos, pots = electrode_config(total_ele, true_csd, t_csd_x, t_csd_y, inpos, lpos)
+        ele_y = ele_pos[:, 1]
+        gdX = 0.01
+        x_lims = [0, 1] #CSD estimation place
+        np.random.seed(srcs)
+        pots += (np.random.rand(total_ele)*np.max(abs(pots))-np.max(abs(pots))/2)*noise
+
+        k, est_csd, est_pot = do_kcsd(ele_y, pots, h=1., gdx=gdX,
+                                      xmin=x_lims[0], xmax=x_lims[1], n_src_init=1e4)
+
+        save_as = nm + '_noise' + str(np.round(noise*100, 1))
+        if name == 'lc':
+            m_norm = k.m_norm
+            m_resi = k.m_resi
+            curve_surf = k.curve_surf
+        else:
+            m_norm = k.errs[0]
+            m_resi = np.arange(len(k.errs[1]))
+            curve_surf = k.errs
+        title = [str(k.lambd), str(k.R), str(noise), nm]
+        RMS_wek[i] = make_plots(title, m_norm, m_resi, true_csd, curve_surf,
+                               ele_y, pots, k.estm_x, k.estm_x, est_pot, est_csd, save_as)
+        LandR[0, i] = k.lambd
+        LandR[1, i] = k.R
+    return RMS_wek, LandR
+
+
+if __name__=='__main__':
+    saveDir = "./LCurve/"
+    total_ele = 32
+    name = 'lc'
+    src_width = 0.001
+    noise_lvl = np.linspace(1e-2, 1, 19)
+    inpos = [0.5, 0.1]#od dolu
+    lpos = [0.5, 0.8]
+    ery = np.linspace(0.01, 0.1, 10)
+    for src in range(10):
+        print('noise seed:', src)
+        mypath = saveDir + name + str(src) + '/'
+        if not os.path.isdir(mypath):
+            os.makedirs(mypath)
+        os.chdir(mypath)
+        RMS_wek, LandR = main_loop(src_width, total_ele, inpos,
+                                   lpos, name, noise=noise_lvl, srcs=src)
+        df = DataFrame({'RMS': RMS_wek.tolist(), 'Lambda': LandR[0].tolist(),
+                        'R' : LandR[1].tolist(), 'noise' : noise_lvl.tolist()})
+        df.to_excel(name + str(src) + '_.xlsx', sheet_name='sheet1', index=False)