find_amplitude_parameters.py 4.04 KB
 Philipp Arras committed Nov 07, 2019 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 ``````# This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . # # Copyright(C) 2013-2019 Max-Planck-Society # Author: Philipp Arras # # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik. import numpy as np import nifty5 as ift import matplotlib.pyplot as plt def _default_pspace(dom): return ift.PowerSpace(dom.get_default_codomain()) if __name__ == '__main__': np.random.seed(42) `````` Philipp Frank committed Nov 14, 2019 31 `````` fa = ift.CorrelatedFieldMaker.make(10, 0.1, '') `````` Philipp Arras committed Nov 07, 2019 32 33 `````` n_samps = 20 slope_means = [-2, -3] `````` Philipp Frank committed Nov 13, 2019 34 `````` fa.add_fluctuations(ift.RGSpace(128, 0.1), 10, 2, 1, 1e-6, `````` Philipp Arras committed Nov 07, 2019 35 36 37 `````` 2, 1e-6, slope_means[0], 0.2, 'spatial') # fa.add_fluctuations(_default_pspace(ift.RGSpace((128, 64))), 10, 2, 1, # 1e-6, 2, 1e-6, slope_means[0], 0.2, 'spatial') `````` Philipp Frank committed Nov 14, 2019 38 `````` fa.add_fluctuations(ift.RGSpace(32), 3, 5, 1, 1e-6, 2, `````` Philipp Arras committed Nov 07, 2019 39 `````` 1e-6, slope_means[1], 1, 'freq') `````` Philipp Frank committed Nov 14, 2019 40 `````` correlated_field = fa.finalize() `````` Philipp Arras committed Nov 07, 2019 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 `````` amplitudes = fa.amplitudes plt.style.use('seaborn-notebook') tgt = correlated_field.target if len(tgt.shape) == 1: fig, axes = plt.subplots(nrows=1, ncols=2) fig.set_size_inches(20, 10) else: fig, axes = plt.subplots(nrows=3, ncols=3) fig.set_size_inches(20, 16) axs = (ax for ax in axes.ravel()) for ii, aa in enumerate(amplitudes): ax = next(axs) pspec = aa**2 ax.set_xscale('log') ax.set_yscale('log') for _ in range(n_samps): fld = pspec(ift.from_random('normal', pspec.domain)) klengths = fld.domain[0].k_lengths ycoord = fld.to_global_data_rw() ycoord[0] = ycoord[1] ax.plot(klengths, ycoord, alpha=1) ymin, ymax = ax.get_ylim() color = plt.rcParams['axes.prop_cycle'].by_key()['color'][0] lbl = 'Mean slope (k^{})'.format(2*slope_means[ii]) for fac in np.linspace(np.log(ymin), np.log(ymax**2/ymin)): xs = np.linspace(np.amin(klengths[1:]), np.amax(klengths[1:])) ys = xs**(2*slope_means[ii])*np.exp(fac) xs = np.insert(xs, 0, 0) ys = np.insert(ys, 0, ys[0]) ax.plot(xs, ys, zorder=1, color=color, linewidth=0.3, label=lbl) lbl = None ax.set_ylim([ymin, ymax]) ax.set_xlim([None, np.amax(klengths)]) ax.legend() if len(tgt.shape) == 2: foo = [] for ax in axs: pos = ift.from_random('normal', correlated_field.domain) fld = correlated_field(pos).to_global_data() foo.append((ax, fld)) mi, ma = np.inf, -np.inf for _, fld in foo: mi = min([mi, np.amin(fld)]) ma = max([ma, np.amax(fld)]) nxdx, nydy = tgt.shape if len(tgt) == 2: nxdx *= tgt[0].distances[0] nydy *= tgt[1].distances[0] else: nxdx *= tgt[0].distances[0] nydy *= tgt[0].distances[1] for ax, fld in foo: im = ax.imshow(fld.T, extent=[0, nxdx, 0, nydy], aspect='auto', origin='lower', vmin=mi, vmax=ma) fig.colorbar(im, ax=axes.ravel().tolist()) elif len(tgt.shape) == 1: ax = next(axs) flds = [] for _ in range(n_samps): pos = ift.from_random('normal', correlated_field.domain) ax.plot(correlated_field(pos).to_global_data()) plt.savefig('correlated_fields.png') plt.close()``````