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_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()
