Esempio n. 1
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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()
Esempio n. 2
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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
Esempio n. 3
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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')
Esempio n. 4
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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')
Esempio n. 5
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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()
Esempio n. 6
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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')
Esempio n. 7
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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')
Esempio n. 8
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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()
Esempio n. 9
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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')
Esempio n. 10
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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()
Esempio n. 11
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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()
Esempio n. 12
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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()
Esempio n. 13
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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)