unstable weight distribution - unedited version. [#3]

fx_brownian-motion_unstable-weight-distribution.py

@@ -0,0 +1,126 @@
+
+import argparse, sys, os, itertools, pickle, matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as pl
+from matplotlib import rc
+
+rc('text', usetex=True)
+pl.rcParams['text.latex.preamble'] = [
+    r'\usepackage{tgheros}',    # helvetica font
+    r'\usepackage{sansmath}',   # math-font matching helvetica
+    r'\sansmath'                # actually tell tex to use it!
+    r'\usepackage{siunitx}',    # micro symbols
+    r'\sisetup{detect-all}',    # force siunitx to use the fonts
+]  
+
+import numpy as np
+from decimal import Decimal
+from scipy.stats import norm, lognorm
+
+
+def pdf_figure(enlist,svfix):
+
+    nbins = 125
+
+    pl.close()
+    fig = pl.figure()
+
+    s = 150
+    ax_lines, ax_cols = 1,1
+    axs = {}
+    for x,y in itertools.product(range(ax_lines),range(ax_cols)):
+        axs['%d,%d'%(x+1,y+1)] = pl.subplot2grid((ax_lines, ax_cols), (x, y))
+
+    fig.set_size_inches(1920/s*ax_lines/4,1080/s*ax_cols/3)
+
+    data_dirs = sorted(['data/brownian-motion/200323_180732_brownian-motion_get-weight-distribution-at-differ-Nstep-vals/data/'+pth for pth in next(os.walk("data/brownian-motion/200323_180732_brownian-motion_get-weight-distribution-at-differ-Nstep-vals/data/"))[1]])
+
+    for dpath in data_dirs:
+
+        print('Found ', dpath)
+
+        try:
+            with open(dpath+'/namespace.p', 'rb') as pfile:
+                nsp=pickle.load(pfile)
+
+            with open(dpath+'/kx.p', 'rb') as pfile:
+                kx=np.array(pickle.load(pfile))
+
+            if nsp['Nsteps'] in enlist:
+
+
+                log_weights = np.log10(kx[:,0][kx[:,0]>0.000001])
+                log_weights = kx[:,0]
+
+
+                # axs['1,1'].hist(log_weights, bins=nbins,
+                #                 density=True)
+
+                text=r'$t_{\text{measure}} = ' + str(nsp['Nsteps']) +'$'
+
+                floc, fscale = norm.fit(log_weights)
+                f_rv = norm(loc=floc, scale=fscale)
+                xs = np.linspace(start=np.min(log_weights),
+                                 stop=np.max(log_weights),
+                                 num = 1000)
+                axs['1,1'].plot(xs, f_rv.pdf(xs), lw=1.5,
+                                linestyle='-', label=text)
+
+
+                # axs['1,1'].text(1.1, 0.95, text,
+                #                 horizontalalignment='left',
+                #                 verticalalignment='top',
+                #                 linespacing = 1.95,
+                #                 fontsize=10,
+                #                 bbox={'boxstyle': 'square, pad=0.3',
+                #                       'facecolor':'white', 'alpha':1,
+                #                       'edgecolor':'none'},
+                #                 transform = axs['1,1'].transAxes,
+                #                 clip_on=False)
+
+
+        except FileNotFoundError:
+            print(dpath[-4:], "reports: Error loading namespace")
+
+
+
+
+    axs['1,1'].spines['right'].set_visible(False)
+    axs['1,1'].spines['top'].set_visible(False)
+    axs['1,1'].yaxis.set_ticks_position('left')
+    axs['1,1'].xaxis.set_ticks_position('bottom')
+
+    axs['1,1'].set_xlabel(r'$X(t_{\text{measure}})$')
+    axs['1,1'].set_ylabel('probability density')
+
+
+    # text = r'$\mu_{b}=' + '%.5E $' % Decimal(nsp['bn_mu']) +\
+    #        '\n' + r'$\sigma_{b}=' + '%.2E $' % Decimal(nsp['bn_sig']) +\
+    #        '\n ---------------- ' +\
+    #        '\n' + r'$N_{\text{process}} = ' + str(nsp['Nprocess']) +'$' +\
+    #        '\n' + r'$N_{\text{steps}} = ' + str(nsp['Nsteps']) +'$'
+
+    box = axs['1,1'].get_position()
+    axs['1,1'].set_position([box.x0, box.y0, box.width * 0.8, box.height])
+
+    axs['1,1'].legend(frameon=False, prop={'size': 10}, loc='center left',
+              labelspacing=1.15, borderpad=1.25, bbox_to_anchor=(1, 0.5))
+
+    # axs['1,1'].set_xlim(-0.15, 29)
+    # axs['1,1'].set_ylim(-0.15, 3.1)
+
+    fname = os.path.splitext(os.path.basename(__file__))[0]
+    fig.savefig("{}.pdf".format(fname), dpi=300, bbox_inches='tight')
+
+
+
+
+
+
+if __name__ == "__main__":
+
+
+    # pdf_figure([500, ], '01')
+    # pdf_figure([500, 1000], '02')
+    # pdf_figure([500, 1000, 5000], '03')
+    pdf_figure([500, 1000, 5000, 10000], '04')