clean version for tag

main_heat_map.py

@@ -22,7 +22,7 @@
 
 # use dataset creation
 # size of the sample
-n_sample = 10 ** 4  # testing on 3, final run on 4
+n_sample = 10 ** 3  # testing on 3, final run on 4
 file_name = 'TS_data_100000.npz'
 try:
     np.load(file_name)
@@ -44,11 +44,6 @@
 ninety_percentile = int(np.ceil(len(alpha1)*0.9))
 alphaMax = np.array([alpha1[ninety_percentile], alpha2[ninety_percentile]])
 
-plt.figure()
-dsgrn_plot(a, alphaMax)
-plt.title('all parameters')
-plt.savefig('all_parameters.pdf')
-
 parameter_full = np.empty(shape=[0, 5])
 lowest_hill = np.empty(0)
 bad_parameters = np.empty(shape=[0, 5])
@@ -99,61 +94,12 @@
 print('Number of saddles', len(parameter_full), 'out of ', n_sample)
 print('Number of parameters with multiple saddles', len(multiple_saddles), 'out of ', n_sample)
 
-"""if bad_candidates is not None:
-    fig1 = plt.figure()
-    dsgrn_plot(bad_candidates, 10)
-    plt.title('bad candidates')
-
-if boring_parameters is not None:
-    fig1 = plt.figure()
-    dsgrn_plot(boring_parameters, 10)
-    plt.title('No saddle detected')
-"""
-"""
-parameter_DSGRN = parameter_to_DSGRN_coord(parameter_full)
-parameter_DSGRN = np.array([parameter_DSGRN[0], parameter_DSGRN[1]])
-unique_DSGRN = np.unique(parameter_DSGRN.round(decimals=5), axis=1)
-average_sol = np.empty(0)
-average_sol_long = 0 * solutions
-for j in unique_DSGRN.T:
-    # work in progress
-    index_solution_j = np.where(abs(parameter_DSGRN[0, :] - j[0]) < 5 * 10 ** -5)
-    index_solution_loc = np.where(abs(parameter_DSGRN[1, :] - j[1]) < 5 * 10 ** -5)
-    index_solution_j = np.intersect1d(index_solution_j, index_solution_loc)
-    if len(index_solution_j) > 0:
-        average_sol = np.append(average_sol, np.mean(solutions[index_solution_j]))
-        average_sol_long[index_solution_j] = np.mean(solutions[index_solution_j])
-    else:
-        print('wrong')
-"""
 
 plt.figure()
 dsgrn_heat_plot(parameter_full, np.minimum(lowest_hill, 100), alphaMax=alphaMax)
 plt.title('dsgrn_heat_plot')
 plt.savefig('dsgrn_heat_plot.pdf')
 
-plt.figure()
-dsgrn_contour_plot(parameter_full, lowest_hill, alphaMax=alphaMax)
-plt.title('dsgrn_contour_plot')
-plt.savefig('dsgrn_contour_plot.pdf')
-
-plt.figure()
-dsgrn_plot(parameter_full, alphaMax=alphaMax)
-plt.title('dsgrn_plot')
-plt.savefig('dsgrn_plot.pdf')
-
-if len(multiple_saddles) > 0:
-    plt.figure()
-    dsgrn_plot(multiple_saddles, alphaMax=alphaMax)
-    plt.title('multiple_saddles')
-    plt.savefig('multiple_saddles.pdf')
-
-if len(bad_parameters) > 0:
-    plt.figure()
-    dsgrn_plot(bad_parameters, alphaMax=alphaMax)
-    plt.title('bad_parameters')
-    plt.savefig('bad_parameters.pdf')
-
 plt.figure()
 dsgrn_plot(a, color='tab:blue', alphaMax=alphaMax, alpha=1)
 dsgrn_plot(parameter_full, color='tab:green', alphaMax=alphaMax)