def interd_quiet_grids():
graphs.GRAPH_OUTPUT_FORMAT = 'png'
def init_simwrap():
sim = SimWrap(ctrl, p, cdict, pdict);
sim.set_all_nodisp()
sim.TO_show_clusters = False
sim.ctrl.saveall = False
return sim
def graph_mods():
ax.set_xlabel('Meters')
ax.set_ylabel('Meters')
ax.set_aspect('equal')
# Random grid
graphs.change_fontsize(graphs.FONTSIZE + 2)
ctrl, p, cdict, pdict = dec_grids()
ctrl.quiet_selection = 'random'
ctrl.quiet_nodes = ctrl.nodecount - 5
ctrl.steps = 1
sim = init_simwrap()
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
sim.simulate()
ax = graphs.spatial_grid(ctrl, unit='m', s=12, show_broadcast=True)
graph_mods()
graphs.save('interd_grid_random')
graphs.show()
# cluster grid
ctrl, p, cdict, pdict = dec_grids()
ctrl.quiet_selection = 'kmeans'
ctrl.quiet_nodes = ctrl.nodecount - 5
ctrl.steps = 1
sim = init_simwrap()
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
sim.simulate()
ax = graphs.spatial_grid(ctrl, unit='m', s=12, show_broadcast=True)
graph_mods()
graphs.save('interd_grid_kmeans')
graphs.show()
# sensing grid
ctrl, p, cdict, pdict = dec_grids()
ctrl.quiet_selection = 'contention'
ctrl.steps = 40
sim = SimWrap(ctrl, p, cdict, pdict);
sim.TO_show_clusters = False
sim.ctrl.saveall = False
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
sim.simulate()
ax = graphs.spatial_grid(ctrl, unit='m', s=12, show_broadcast=True)
graph_mods()
graphs.save('interd_grid_sensing')
graphs.show()
def zero_padded_crosscorr():
fontsize_tmp = graphs.FONTSIZE
graphs.change_fontsize(15)
p = pinit64_no_pulse_shape()
ax = graphs.crosscorr(p, savename='latex_figures/discrete_crosscorr');
graphs.show()
graphs.change_fontsize(fontsize_tmp)
return ax
def thesis_cavg_vs_noise():
# 150 sample per point
alldates = db.fetch_dates([20160830143035701, 20160830162947428]) # zc vs noise_power
dates = [alldates[0], alldates[-1]]
graphs.change_fontsize(15)
ax = graphs.scatter_range(dates, ['noise_power', 'good_link_ratio'], color='b')
ax.set_xlabel("Noise Power (dBm)")
ax.set_ylabel("$C$")
#ax.set_ylim((0.25,0.85))
graphs.save('latex_figures/thesis_cavg_vs_noise')
def thesis_cavg_vs_zc():
# 150 sample per point
alldates = db.fetch_dates([20160825232333852, 20160826024432633]) # cavg vs zclen
dates = [alldates[0], alldates[-1]]
graphs.change_fontsize(15)
ax = graphs.scatter_range(dates, ['zc_len', 'good_link_ratio'], color='b')
ax.set_xlabel("Length of ZC sequence $N$")
ax.set_ylabel("$C$")
ax.set_ylim((0.25,0.85))
graphs.save('latex_figures/thesis_cavg_vs_zc')
def interd_cavg_vs_nodecount():
# 150 sample per point
graphs.GRAPH_OUTPUT_FORMAT = 'png'
graphs.change_fontsize(graphs.FONTSIZE + 2)
alldates = db.fetch_dates([20160825101515414, 20160825135628487]) # cavg vs nodecount
dates = [alldates[0], alldates[-1]]
ax = graphs.scatter_range(dates, ['nodecount', 'good_link_ratio'], color='k')
ax.set_xlabel("Number of nodes $M$")
ax.set_ylabel("$C$")
graphs.save('interd_cavg_vs_nodecount')
graphs.show()
def thesis_cavg_vs_nodecount():
# 180 sample per point
alldates = db.fetch_dates([20160824001859759, 20160824034043274]) # nodecount vs cavg
dates = [alldates[0], alldates[-1]]
graphs.change_fontsize(15)
ax = graphs.scatter_range(dates, ['nodecount', 'good_link_ratio'], color='b')
ax.set_xlabel("Number of nodes $M$")
ax.set_ylabel("$C$")
ax.set_ylim((0.37,0.92))
graphs.save('latex_figures/thesis_cavg_vs_nodecount')
def thesis_cavg_vs_distance():
# 180 sample per point
alldates = db.fetch_dates([20160824125623027, 20160824143207623]) # dist vs cavg
alldates += db.fetch_dates([20160824155429863, 20160824170025866]) # dist vs cavg (extra)
dates = [alldates[0], alldates[-1]]
graphs.change_fontsize(15)
ax = graphs.scatter_range(dates, ['max_dist_from_origin', 'good_link_ratio'], color='b')
ax.set_xlabel("Side of square area (Meters)")
ax.set_ylabel("$C$")
tick_locs = [500,750,1000,1250,1500]
tick_lbls = list(map(str, tick_locs))
ax.set_xticks(tick_locs)#, minor=False)
ax.set_xticklabels(tick_lbls)
graphs.save('latex_figures/thesis_cavg_vs_distance')
def interd_compare_quiet():
graphs.GRAPH_OUTPUT_FORMAT = 'png'
graphs.change_fontsize(graphs.FONTSIZE + 2)
alldates = db.fetch_dates([20160822172521531, 20160822215536865]) # 960sims quiet compare
dates = [alldates[0], alldates[-1]]
labels = []
labels.append('Random')
labels.append('Clustering')
labels.append('Sensing')
ax = graphs.scatter_range(dates, ['max_dist_from_origin', 'good_link_ratio'], multiplot='quiet_selection', legend_labels=labels);
ax.set_xlabel("Side of square area (m)")
ax.set_ylabel("$C$")
graphs.save('interd_compare_quiet')
graphs.show()
def interd_compare_r12():
graphs.change_fontsize(graphs.FONTSIZE + 2)
ctrl, p, cdict, pdict = dec_dpll()
sim = SimWrap(ctrl, p, cdict, pdict)
sim.TO_show_clusters = False
sim.ctrl.saveall = False
sim.simulate()
sim.post_sim_plots(save_TO='dpll_theta_evol')
ctrl, p, cdict, pdict = dec_r12()
sim = SimWrap(ctrl, p, cdict, pdict)
sim.TO_show_clusters = False
sim.ctrl.saveall = False
sim.simulate()
sim.post_sim_plots(save_TO='r12_theta_evol')
def interd_dpll_vs_r12():
# 150 sample per point
graphs.GRAPH_OUTPUT_FORMAT = 'png'
graphs.change_fontsize(graphs.FONTSIZE + 2)
alldates = db.fetch_dates([20160828200017740, 20160828205450849]) # r12 vs dpll
dates = [alldates[0], alldates[-1]]
labels = []
labels.append('R12')
labels.append('DPLL')
ax = graphs.scatter_range(dates, ['nodecount', 'good_link_ratio'], multiplot='peak_detect', legend_labels=labels)
ax.set_xlabel("Number of nodes (M)")
ax.set_ylabel("$C$")
graphs.save('interd_dpll_vs_r12')
graphs.show()
def interd_cavg_vs_distance():
# 150 sample per point
graphs.GRAPH_OUTPUT_FORMAT = 'png'
graphs.change_fontsize(graphs.FONTSIZE + 2)
alldates = db.fetch_dates([20160825141108531, 20160825183253474]) # cavg vs dist
dates = [alldates[0], alldates[-1]]
ax = graphs.scatter_range(dates, ['max_dist_from_origin', 'good_link_ratio'], color='k')
ax.set_xlabel("Side of square area (Meters)")
ax.set_ylabel("$C$")
tick_locs = [500,750,1000,1250,1500]
tick_lbls = list(map(str, tick_locs))
ax.set_xticks(tick_locs)#, minor=False)
ax.set_xticklabels(tick_lbls)
graphs.save('interd_cavg_vs_distance')
graphs.show()
def interd_dpll_final_compare():
#alldates = db.fetch_dates([20160829122809568, 20160829152128098]) # plain dpll vs contention
graphs.GRAPH_OUTPUT_FORMAT = 'png'
graphs.change_fontsize(15)
alldates = db.fetch_dates([20160828200017740, 20160828205450849]) # r12 vs dpll
dates = [alldates[0], alldates[-1]]
#extra ( add to scatter_range)
#sens_dates = db.fetch_dates([20160829122809568, 20160829152128098]) # plain dpll vs contention
#new_fetch_dict = {'date':sens_dates, 'quiet_selection':['contention']}
#raw_data = db.fetch_matching(new_fetch_dict, collist)
#datalist.append(np.array(raw_data))
#labels.append('Sensing')
labels = []
labels.append('R12')
labels.append('DPLL')
labels.append('DPLL-S')
ax = graphs.scatter_range(dates, ['nodecount', 'good_link_ratio'], multiplot='peak_detect', legend_labels=labels)
ax.set_xlabel("Number of nodes (M)")
ax.set_ylabel("$C$")
graphs.save('interd_dpll_final_compare')
graphs.show()
def highlited_regimes():
"""Highlights the converging vs the drift regime"""
compare_fontsize = 15
compare_aspect=12
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
fontsize_tmp = graphs.FONTSIZE
def run_hair_graph(aspect='auto'):
hair_kwargs = {'y_label':r'$\theta_i$ $(T_0)$', 'show_clusters':False, 'savename':''}
hair_args = (
(ctrl.sample_inter , ctrl.theta_inter, ctrl),
hair_kwargs,
)
ax = graphs.hair(*hair_args[0], **hair_args[1])
ax.set_aspect(aspect)
return ax
# DRIFT REGIME
graphs.change_fontsize(compare_fontsize)
ctrl, p, cdict, pdict = dec_sample_theta()
sim = SimWrap(ctrl, p, cdict, pdict)
sim.conv_min_slope_samples = 15
sim.ctrl.keep_intermediate_values = True
sim.simulate()
ax = run_hair_graph(aspect=compare_aspect)
ax.text(0.85, 0.9, 'No DC', transform=ax.transAxes, fontsize=compare_fontsize, verticalalignment='top', bbox=props)
xmid = 12
xmax = ctrl.steps-13
xmin = 0
ymin, ymax = ax.get_ylim()
ya = ymin-0.06
ax.set_xlim([xmin,xmax])
ax.set_ylim([ya,ymax])
fname = 'latex_figures/init_theta'
graphs.save(fname)
graphs.show()
del ax
graphs.change_fontsize(15)
ax = run_hair_graph()
# Draw biarrows
ax.annotate('', xy=(xmid, ya), xycoords='data',
xytext=(xmin, ya), textcoords='data',
arrowprops=dict(arrowstyle="<->"))
ax.annotate('', xy=(xmax, ya), xycoords='data',
xytext=(xmid, ya), textcoords='data',
arrowprops=dict(arrowstyle="<->"))
ax.plot([xmid, xmid], [ya-1, ymax], 'k--')
# text
ax.text(xmid/2, ya, 'Transient', ha='center', va='bottom' )
ax.text((xmax-xmid)/2 +xmid, ya, 'Drifting', ha='center', va='bottom' )
ax.set_xlim([xmin,xmax])
ax.set_ylim([ya-0.03,ymax])
fname = 'latex_figures/highlighted_regimes'
graphs.save(fname)
graphs.show()
# THETA EXAMPLE
graphs.change_fontsize(compare_fontsize)
ctrl, p, cdict, pdict = dec_sample_theta()
ctrl.prop_correction = True
sim = SimWrap(ctrl, p, cdict, pdict)
sim.conv_min_slope_samples = 15
sim.ctrl.keep_intermediate_values = True
sim.show_CFO = False
sim.show_TO = False
sim.make_cat = False
sim.simulate()
ax = run_hair_graph(aspect=compare_aspect)
ax.text(0.85, 0.9, 'Q = 7', transform=ax.transAxes, fontsize=compare_fontsize, verticalalignment='top', bbox=props)
ax.set_xlim([xmin,xmax])
ax.set_ylim([ya,ymax])
fname = 'latex_figures/example_theta'
graphs.save(fname)
graphs.show()
# THETA EXAMPLE with Q=14
graphs.change_fontsize(compare_fontsize)
ctrl, p, cdict, pdict = dec_sample_theta()
ctrl.prop_correction = True
ctrl.pc_b, ctrl.pc_a = lib.hipass_avg(14)
sim = SimWrap(ctrl, p, cdict, pdict)
sim.conv_min_slope_samples = 15
sim.ctrl.keep_intermediate_values = True
sim.show_CFO = False
sim.show_TO = False
sim.make_cat = False
sim.simulate()
ax = run_hair_graph(aspect=compare_aspect)
ax.text(0.85, 0.9, 'Q = 14', transform=ax.transAxes, fontsize=compare_fontsize, verticalalignment='top', bbox=props)
ax.set_xlim([xmin,xmax])
ax.set_ylim([ya,ymax])
fname = 'latex_figures/example_theta_q14'
graphs.save(fname)
graphs.show()
graphs.change_fontsize(fontsize_tmp)