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()
