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How to use converged method in molecule
Best Python code snippet using molecule_python
corrmut_test.py
Source:corrmut_test.py 
...65 ###########66 def test_hard_conv_1(self):67 labels = [1] * 1068 pre_labels = [1] * 1069 converged = corrmut.has_converged(labels, pre_labels, "hard")70 assert converged71 def test_hard_conv_2(self):72 labels = [0] * 1073 pre_labels = [0] * 1074 converged = corrmut.has_converged(labels, pre_labels, "hard")75 assert converged76 def test_hard_conv_3(self):77 labels = [1] * 1078 pre_labels = [0] * 1079 converged = corrmut.has_converged(labels, pre_labels, "hard")80 assert not converged81 def test_hard_conv_4(self):82 labels = [0] * 1083 pre_labels = [1] * 1084 converged = corrmut.has_converged(labels, pre_labels, "hard")85 assert not converged86 def test_hard_conv_5(self):87 labels = [1, 1, 1, 0, 0, 0]88 pre_labels = [0, 0, 0, 1, 1, 1]89 converged = corrmut.has_converged(labels, pre_labels, "hard")90 assert not converged91 def test_hard_conv_6(self):92 labels = [1, 1, 1, 1, 0]93 pre_labels = [1, 1, 1, 1, 1]94 converged = corrmut.has_converged(labels, pre_labels, "hard")95 assert not converged96 def test_hard_conv_7(self):97 labels = [1, 1, 1, 0, 0, 0]98 pre_labels = [1, 1, 1, 0, 0, 0]99 converged = corrmut.has_converged(labels, pre_labels, "hard")100 assert converged101 ###########102 # Soft EM #103 ###########104 def test_soft_conv_1(self):105 labels = [1] * 10106 pre_labels = [1] * 10107 converged = corrmut.has_converged(labels, pre_labels, "soft")108 assert converged109 def test_soft_conv_2(self):110 labels = [0] * 10111 pre_labels = [0] * 10112 converged = corrmut.has_converged(labels, pre_labels, "soft")113 assert converged114 def test_soft_conv_3(self):115 labels = [1] * 10116 pre_labels = [0] * 10117 converged = corrmut.has_converged(labels, pre_labels, "soft")118 assert not converged119 def test_soft_conv_4(self):120 labels = [0] * 10121 pre_labels = [1] * 10122 converged = corrmut.has_converged(labels, pre_labels, "soft")123 assert not converged124 def test_soft_conv_5(self):125 labels = [1, 1, 1, 0, 0, 0]126 pre_labels = [0, 0, 0, 1, 1, 1]127 converged = corrmut.has_converged(labels, pre_labels, "soft")128 assert not converged129 def test_soft_conv_6(self):130 labels = [1, 1, 1, 1, 0]131 pre_labels = [1, 1, 1, 1, 1]132 converged = corrmut.has_converged(labels, pre_labels, "soft")133 assert not converged134 def test_soft_conv_7(self):135 labels = [1, 1, 1, 0, 0, 0]136 pre_labels = [1, 1, 1, 0, 0, 0]137 converged = corrmut.has_converged(labels, pre_labels, "soft")138 assert converged139 def test_soft_conv_8(self):140 labels = [0.99, 0.95, 0.90, 0.95, 0.1, 0.1, 0.001, 1e-3]141 pre_labels = [[0.99, 0.95, 0.90, 0.95, 0.1, 0.1, 0.001, 1e-16]]142 tol = 5e-3143 converged = corrmut.has_converged(labels, pre_labels, "soft", tol=tol)144 assert converged145 def test_soft_conv_9(self):146 labels = [0.99, 0.95, 0.90, 0.95, 0.1, 0.1, 0.001, 1e-3]147 pre_labels = [[0.99, 0.95, 0.90, 0.95, 0.1, 0.1, 0.001, 1e-32]]148 tol = 5e-3149 converged = corrmut.has_converged(labels, pre_labels, "soft", tol=tol)150 assert converged151 def test_soft_conv_10(self):152 # Difference between two arrays equals tolerance,153 # (would not be enough to reach convergence by itself)154 # but amount of observations must also be taken into account!155 labels = [0.99, 0.99, 0.1, 0.1, 0.005]156 pre_labels = [0.99, 0.99, 0.1, 0.1, 0.01]157 tol = 5e-3158 converged = corrmut.has_converged(labels, pre_labels, "soft", tol=tol)159 assert converged160#####################161# Alternative model #162#####################163class TestGetPosteriorLogProbs():164 """165 Class to test the corrmut.get_posterior_logprobs function166 """167 def test_logprobs_1(self):168 # Tested using sklearn 0.19.2169 # Covariation between two toy alignments170 Y = [3, 11, 3, 11, 3, 11]171 X = np.array([[11, 3, 11, 3, 11, 3]])172 exp_logprobs = np.log([0.79807613, 0.79822048, 0.79807613,...
plot_vary_multipliers_v2_data.py
Source:plot_vary_multipliers_v2_data.py
1"""2Plot v2 of Fig. 4's parameter sweep, which varies over overlap length, finger width, initial front gap, and support spring width3"""4import os5file_location = os.path.abspath(os.path.dirname( __file__))6dir_location = os.path.abspath(os.path.join(file_location, '..'))7import sys8sys.path.append(file_location)9sys.path.append(dir_location)10import numpy as np11import matplotlib.pyplot as plt12from scipy.io import loadmat, savemat13from datetime import datetime14plt.rc('font', size=11.5)15plt.rcParams['legend.fontsize'] = 11.516def setup_plot(len_x, len_y, plt_title=None, x_label="", y_label=""):17 fig, axs = plt.subplots(len_x, len_y)18 if plt_title is not None:19 fig.suptitle(plt_title)20 if x_label or y_label:21 # add a big axis, hide frame22 fig.add_subplot(111, frameon=False)23 # hide tick and tick label of the big axis24 plt.tick_params(labelcolor='none', which='both', top=False, bottom=False, left=False, right=False)25 plt.xlabel(x_label, fontsize=12)26 plt.ylabel(y_label, fontsize=12)27 return fig, axs28def display_stats(x_converged, times_converged, label, nom):29 # Get helpful stats for paper30 # print(label, x_converged, times_converged)31 try:32 time_50_up = times_converged[np.where(np.isclose(x_converged, nom*1.25e6))[0][0]]33 time_nominal = times_converged[np.where(np.isclose(x_converged, nom*1.e6))[0][0]]34 time_50_down = times_converged[np.where(np.isclose(x_converged, nom*0.8e6))[0][0]]35 print("{}: con=0.75: {} (Ratio: {}), con=1: {}, con=1.25: {} (Ratio: {})".format(36 label, time_50_down, 1 - time_50_down/time_nominal, time_nominal, time_50_up, 1 - time_50_up/time_nominal37 ))38 except Exception as e:39 print(e)40now = datetime.now()41name_clarifier = "_20211024_01_37_55_vary_multipliers_undercut=0.400_Fes=v2_Fb=v2_modified"42timestamp = now.strftime("%Y%m%d_%H_%M_%S") + name_clarifier43print(timestamp)44filename = "../data/20211024_01_37_55_vary_multipliers_undercut=0.400_Fes=v2_Fb=v2.npy"45fileData = np.load(filename, allow_pickle=True)46process, fingerLcon_range, fingerWcon_range, gfcon_range, supportWcon_range, fingerLnom, fingerWnom, gfnom, supportWnom, data, fig = fileData47plt.close()48fig, axs = setup_plot(2, 2, y_label=r"Time ($\mu$s)")49### fingerL50x_converged, times_converged = data["fingerLpullin"]51x_converged = np.array(x_converged)52times_converged = np.array(times_converged)53idx = np.where(x_converged < 95)54x_converged = x_converged[idx]55times_converged = times_converged[idx]56axs[0, 0].plot(x_converged, times_converged, 'b')57display_stats(x_converged, times_converged, "Pullin fingerLcon", fingerLnom)58x_converged, times_converged = data["fingerLrelease"]59x_converged = np.array(x_converged)60times_converged = np.array(times_converged)61idx = np.where(x_converged < 95)62x_converged = x_converged[idx]63times_converged = times_converged[idx]64axs[0, 0].plot(x_converged, times_converged, 'r')65axs[0, 0].set_title(r"Varying $L_{ol}$", fontsize=12)66axs[0, 0].axvline(fingerLnom*1e6, color='k', linestyle='--')67display_stats(x_converged, times_converged, "Release fingerLcon", fingerLnom)68label = r"$L_{ol}=$" + "{:0.1f}".format(fingerLnom*1e6) + r' $\mu$m'69axs[0, 0].annotate(label, xy=(0.73, 0.96), xycoords='axes fraction', color='k',70 xytext=(0, 0), textcoords='offset points', ha='right', va='top')71axs[0, 0].set_xlabel(r'$L_{ol} (\mu$m)')72### fingerW73x_converged, times_converged = data["fingerWpullin"]74x_converged = np.array(x_converged)75times_converged = np.array(times_converged)76# idx = np.where(x_converged > 3.425)77idx = np.where(np.array(x_converged) > 3.5)78# idx = np.where(x_converged >= 4.)79x_converged = x_converged[idx]80times_converged = times_converged[idx]81axs[0, 1].plot(x_converged, times_converged, 'b')82display_stats(x_converged, times_converged, "Pullin fingerWcon", fingerWnom)83x_converged, times_converged = data["fingerWrelease"]84x_converged = np.array(x_converged)85times_converged = np.array(times_converged)86# idx = np.where(np.array(x_converged) > 3.425)87idx = np.where(np.array(x_converged) > 3.5)88# idx = np.where(x_converged >= 4.)89x_converged = x_converged[idx]90times_converged = times_converged[idx]91axs[0, 1].plot(x_converged, times_converged, 'r')92axs[0, 1].set_title(r"Varying $w_f$", fontsize=12)93axs[0, 1].axvline(fingerWnom*1e6, color='k', linestyle='--')94display_stats(x_converged, times_converged, "Release fingerWcon", fingerWnom)95label = r"$w_f=$" + "{:0.1f}".format(fingerWnom*1e6) + r' $\mu$m'96axs[0, 1].annotate(label, xy=(0.29, 0.6), xycoords='axes fraction', color='k',97 xytext=(0, 0), textcoords='offset points', ha='left', va='top')98axs[0, 1].set_xlabel(r'$w_f (\mu$m)')99### gf0100x_converged, times_converged = data["gfpullin"]101axs[1, 0].plot(x_converged, times_converged, 'b')102display_stats(x_converged, times_converged, "Pullin gf0con", gfnom)103max_x = max(x_converged)104x_converged, times_converged = data["gfrelease"]105x_converged = np.array(x_converged)106times_converged = np.array(times_converged)107idx = np.where(np.array(x_converged) < max_x)108x_converged = x_converged[idx]109times_converged = times_converged[idx]110axs[1, 0].plot(x_converged, times_converged, 'r')111axs[1, 0].set_title(r"Varying $x_0$", fontsize=12)112axs[1, 0].axvline(gfnom*1e6, color='k', linestyle='--')113display_stats(x_converged, times_converged, "Release gf0con", gfnom)114label = r"$x_0=$" + "\n" + "{:0.2f}".format(gfnom*1e6) + r' $\mu$m'115axs[1, 0].annotate(label, xy=(0.57, 0.96), xycoords='axes fraction', color='k',116 xytext=(0, 0), textcoords='offset points', ha='left', va='top')117axs[1, 0].set_xlabel(r'$x_0 (\mu$m)')118### supportW119x_converged, times_converged = data["supportWpullin"]120axs[1, 1].plot(x_converged, times_converged, 'b')121display_stats(x_converged, times_converged, "Pullin supportWcon", supportWnom)122x_converged, times_converged = data["supportWrelease"]123axs[1, 1].plot(x_converged, times_converged, 'r')124axs[1, 1].set_title(r"Varying $w_{spr}$", fontsize=12)125axs[1, 1].axvline(supportWnom*1e6, color='k', linestyle='--')126display_stats(x_converged, times_converged, "Release supportWcon", supportWnom)127label = r"$w_{spr}=$" + "{:0.1f}".format(supportWnom*1e6) + r' $\mu$m'128axs[1, 1].annotate(label, xy=(0.47, 0.96), xycoords='axes fraction', color='k',129 xytext=(0, 0), textcoords='offset points', ha='left', va='top')130axs[1, 1].set_xlabel(r'$w_{spr} (\mu$m)')131axs[1, 1].legend(["Pull-in", "Release"], loc='center right')132plt.tight_layout()133timestamp = "20211024_01_37_55_vary_multipliers_undercut=0.400_Fes=v2_Fb=v2_modified_v4"134# plt.savefig("../figures/" + timestamp + ".png")135# plt.savefig("../figures/" + timestamp + ".pdf")...
library.py
Source:library.py
1'''2 Copyright (C) 2020 Anne Hartebrodt3 This program is free software; you can redistribute it and/or modify4 it under the terms of the GNU General Public License as published by5 the Free Software Foundation; either version 2 of the License, or6 (at your option) any later version.7 This program is distributed in the hope that it will be useful,8 but WITHOUT ANY WARRANTY; without even the implied warranty of9 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the10 GNU General Public License for more details.11 You should have received a copy of the GNU General Public License along12 with this program; if not, write to the Free Software Foundation, Inc.,13 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.14 Authors: Anne Hartebrodt15'''16import numpy as np17import scipy as sc18import svd.shared_functions as sh19import scipy.linalg as la20import svd.comparison as co21def local1(data, G_i):22 return np.dot(data, G_i)23def local2(data, H_pooled, G_previous):24 return G_previous + np.dot(data.T, H_pooled)25def pooling(H_all):26 H_pooled = H_all[0]27 for h in H_all[1:len(H_all)]:28 H_pooled = H_pooled + h29 #H_pooled = H_pooled / len(H_all)30 H_pooled = H_pooled / np.linalg.norm(H_pooled)31 return H_pooled32def standalone(data, k=10):33 G_i = sh.generate_random_gaussian(data.shape[1], k) # phi134 G_i, Q = la.qr(G_i, mode='economic')35 converged = False36 previous = G_i37 previous_h = sh.generate_random_gaussian(data.shape[0], k)38 iterations = 039 while not converged:40 iterations = iterations+141 print(iterations)42 H_i = np.dot(data, G_i) # YiPhii , gamma if standalone43 G_i = np.dot(data.T, H_i) + previous44 G_i, Q = la.qr(G_i, mode='economic') # 'compute residuals'45 converged, sum = convergence_checker(H_i, previous_h, epsilon=0.0000001)46 previous_h = H_i47 previous = G_i48 return G_i49def convergence_checker_d(current, previous, epsilon=0.000001):50 '''51 Checks the convergence52 Args:53 H_i:54 N_list:55 previous:56 epsilon:57 Returns: -1 if convergence not reached58 '''59 sum = 060 for i in range(current.shape[1]):61 ra = np.dot(current[:, i].T, current[:, i]) / sc.linalg.norm(current[:, i])62 sum = np.abs(ra)63 if np.abs(sum-previous) < epsilon:64 return -165 else:66 return ra67def convergence_checker_angle(current, previous, epsilon=0.000001, return_converged=False):68 """69 Check if angle between consecutive eigenvectors is nearing 070 Args:71 current: Current eigenvalue guesss72 previous: Previous eigenvalue guess73 epsilon: tolerance74 return_converged: verbosity75 Returns:76 """77 sum = 078 converged = True79 converged_eigenvectors = []80 deltas = []81 for i in range(current.shape[1]):82 ra = np.dot(current[:,i].T, previous[:,i])83 if ra >= 1-epsilon:84 converged_eigenvectors.append(i)85 else:86 # if one of the eigenvalues has not converged set overall convergence to true87 converged = False88 if return_converged:89 return converged, sum, converged_eigenvectors, deltas90 else:91 return converged, sum92def convergence_checker(current, previous, epsilon=0.000001, return_converged=False):93 """94 Convergence checked via Raleigh coefficient.95 Args:96 current:97 previous:98 epsilon:99 return_converged:100 Returns:101 """102 sum = 0103 converged = True104 converged_eigenvectors = []105 deltas = []106 for i in range(current.shape[1]):107 ra = np.dot(current[:,i].T, current[:,i])/sc.linalg.norm(current[:,i])108 rap = np.dot(previous[:,i].T, previous[:,i])/sc.linalg.norm(previous[:,i])109 sum = sum + np.abs(ra-rap)110 deltas.append(np.abs(ra-rap))111 if np.abs(ra-rap) > epsilon:112 converged=False113 else:114 converged_eigenvectors.append(i)115 if return_converged:116 return converged, sum, converged_eigenvectors, deltas117 else:118 return converged, sum119def convergence_checker_a(current, previous,alpha_norm, alpha_norm_prev, epsilon=0.000001, return_converged=False):120 """121 weird convergence criterion emplyed by Guo et al.122 Args:123 current: Current H124 previous: previous H125 alpha_norm: Current G norm126 alpha_norm_prev: previous G norm127 epsilon: tolerance128 return_converged: Which values to return129 Returns:130 """131 sum = 0132 converged = True133 converged_eigenvectors = []134 deltas = []135 for i in range(current.shape[1]):136 ra = np.dot(current[:,i].T, current[:,i])/alpha_norm[i]137 rap = np.dot(previous[:,i].T, previous[:,i])/alpha_norm_prev[i]138 sum = sum + np.abs(ra-rap)139 deltas.append(np.abs(ra-rap))140 if np.abs(ra-rap) > epsilon:141 converged=False142 else:143 converged_eigenvectors.append(i)144 if return_converged:145 return converged, sum, converged_eigenvectors, deltas146 else:147 return converged, sum148def standalone2(data, first):149 G_i = sh.generate_random_gaussian(data.shape[1], 1)150 G_i = G_i - np.dot(np.inner(G_i, first.T) , first.T)151 print(np.asarray(G_i).T)152 print(first)153 print(co.angle(np.asarray(G_i).T, np.asarray(first).T))154 Q, R = la.qr(np.asarray(np.concatenate([ first.T,G_i], axis = 1)))155 print(Q[:,0])156 G_i = Q[:,1]157 converged = False158 previous = G_i159 previous_h = sh.generate_random_gaussian(data.shape[0], 1)160 iterations = 0161 while not converged:162 iterations = iterations+1163 print(iterations)164 H_i = np.dot(data, G_i) # YiPhii , gamma if standalone165 G_i = np.dot(data.T, H_i) + previous166 Q, R = la.qr(np.asarray(np.stack([first.flatten(),G_i.T])).T)167 G_i = Q[:, 1]168 converged = convergence_checker(H_i, previous_h)169 previous_h = H_i170 previous = G_i171 return G_i172def get_initial_eigenvector_k(V):173 '''174 ap = a -sum over k-1 <a, ai>ai175 Args:176 V:177 Returns:178 '''179 a = sh.generate_random_gaussian(1, V.shape[0])180 sum = np.zeros(V.shape[0])181 for v in range(V.shape[1]):182 sum = sum + np.dot(np.dot(a, v), v)183 ap = a - sum...
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