def saveGraphForResults(filename,metric): """ """ dir=ensureTrailingBackslash(getFileDir(filename)) drawSimilaritiesGraph(filename,metric,True) name=getFileName(filename) plt.savefig(dir+name+'.png', bbox_inches='tight') plt.close()
def drawSimilaritiesGraph(filename,metric, smooth_graph=False): """ Draws graph from CSV file """ dir=getFileDir(filename) if dir=="": filename=cp.Corpus.dir_output+filename print "Drawing graph for",filename data=pandas.read_csv(filename) ## columns=AZ_ZONES_LIST+[metric] columns=[u"ilc_CSC_"+zone for zone in CORESC_LIST]+[metric] if columns[0] not in data.columns: columns=[zone for zone in CORESC_LIST]+[metric] if columns[0] not in data.columns: columns=[zone for zone in AZ_ZONES_LIST]+[metric] ## columns=["OWN","OTH","CTR","BKG","AIM"]+[metric] ## columns=["OWN","OTH","CTR"]+[metric] ## f = lambda x: mode(x, axis=None)[0] ## print data[columns].head(20).apply(f) ## print data.describe() numrows=data.shape[0] # (y,x) ## print data[columns].head(10).mean() data=data.sort(metric, ascending=False) # smoothing function rows_to_group=100 ## rows_to_group=numrows/700 f=lambda x:100-(x/rows_to_group) results=[] ## index=0 ## while index < numrows: ## means=data[columns].iloc[index:index+rows_to_group].mean() #### print means[metric] ## index+=rows_to_group ## results.append(means) ## results.reverse() if smooth_graph: df=data[columns].groupby([f], sort=True) results=df[columns].mean() else: data["g_"+metric]=data[metric] results=data.groupby(["g_"+metric])[columns].mean() ## colors = ["windows blue", "amber", "greyish", "faded green", "dusty purple", "baby pink"] ## sns.palplot(sns.xkcd_palette(colors)) ## flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71", "#002354", ""] ## sns.palplot(sns.color_palette(flatui)) ## print sns.color_palette("Set2") ## sns.set_style("white") sns.set_style("whitegrid") ## sns.set_style("whitegrid", {"grid.linewidth": .5}) ## sns.set_context("talk") sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 2.5}) ## sns.set_palette(sns.color_palette("hls", 7)) ## sns.set_palette(sns.color_palette("husl", 7)) ## sns.set_palette("bright",n_colors=14,desat=0.9) sns.set_palette(sns.color_palette("gist_rainbow", 11),11,0.9) ## sns.palplot(sns.color_palette()) ## sns.palplot(sns.color_palette(n_colors=14) ) results_data=DataFrame(results) ## results_data.plot(x=metric, kind="line") results_data[columns].plot(title=filename,fontsize=20, x=metric) sns.despine()