def plot_four(plot_data):
fig = plt.figure(figsize=(20, 10))
# gs = gridspec.GridSpec(1, 2, height_ratios=[1, 2])
ax = fig.add_subplot(223, projection='3d')
ax.scatter(plot_data['sx'], plot_data['sy'], plot_data['sz'])
ax.plot(plot_data['sx'], plot_data['sy'], plot_data['sz'], color='b')
ax.view_init(azim=0, elev=90) #xy plane
plt.xticks(fontsize=10)
ax.set_title('Displacement Projection in xy Plane',size=20)
ax2 = fig.add_subplot(224, projection='3d')
ax2.scatter(plot_data['sx'], plot_data['sy'], plot_data['sz'])
ax2.plot(plot_data['sx'], plot_data['sy'], plot_data['sz'], color='b')
ax2.view_init(azim=0, elev=45)
ax2.set_title('Displacement',size=20)
ax3 = fig.add_subplot(221)
# 50 represents number of points to make between T.min and T.max
xnew = np.linspace(0,8,50)
spl = make_interp_spline(pd.Series(range(9)), plot_data['tilt1'], k=3) # type: BSpline
x = spl(xnew)
spl = make_interp_spline(pd.Series(range(9)), plot_data['tilt2'], k=3) # type: BSpline
y = spl(xnew)
spl = make_interp_spline(pd.Series(range(9)), plot_data['compass'], k=3) # type: BSpline
z = spl(xnew)
ax3.plot(x,"b-",label='tilt1')
ax3.plot(y,"r-",label='tilt2')
ax3.plot(z,"g-",label='compass')
ax3.legend(loc="lower left",prop={'size': 20})
ax3.set_title('Orientation Plot (degree)',size=20)
ax3.tick_params(labelsize=20)
ax4 = fig.add_subplot(222)
# x = gaussian_filter1d(plot_data['ax'], sigma=1)
# y = gaussian_filter1d(plot_data['ay'], sigma=1)
# z = gaussian_filter1d(plot_data['az'], sigma=1)
# mag = gaussian_filter1d(plot_data['accelerometer'], sigma=1)
spl = make_interp_spline(pd.Series(range(9)), plot_data['ax'], k=3) # type: BSpline
x = spl(xnew)
spl = make_interp_spline(pd.Series(range(9)), plot_data['ay'], k=3) # type: BSpline
y = spl(xnew)
spl = make_interp_spline(pd.Series(range(9)), plot_data['az'], k=3) # type: BSpline
z = spl(xnew)
spl = make_interp_spline(pd.Series(range(9)), plot_data['accelerometer'], k=3) # type: BSpline
mag = spl(xnew)
ax4.plot(x/1000,"c--",label='ax')
ax4.plot(y/1000,"g--",label='ay')
ax4.plot(z/1000,"b--",label='az')
ax4.plot(mag,"r-",label='Acc')
ax4.legend(loc="lower left",prop={'size': 20})
ax4.set_title('Acceleration Plot (g)',size=20)
ax4.tick_params(labelsize=20)
plt.tight_layout()
plt.show()
fig.savefig('FourInOne.png')
def multiplot_gen_property_type():
#
# font = {'family': 'Liberation Serif',
# 'weight': 'normal',
# 'size': 15
# }
#
# # play around with the font size if it is too big or small
# matplotlib.rcParams['axes.titlesize'] = 12
# matplotlib.rcParams['axes.labelsize'] = 12
# matplotlib.rc('font', **font)
# # matplotlib.rcParams['text.usetex'] = True
# matplotlib.rcParams['pdf.fonttype'] = 42
# matplotlib.rcParams['pdf.use14corefonts'] = True
x = list(data.keys())
y1=[]
y2=[]
y3=[]
y4=[]
y5=[]
y6=[]
for year in data.keys():
for option_name, count in data[year].items():
if option_name == 'domain':
y1.append(count)
if option_name == 'sitekey':
y2.append(count)
if option_name == 'third-party':
y3.append(count)
if option_name == 'websocket':
y4.append(count)
if option_name == 'webrtc':
y5.append(count)
if option_name == 'csp':
y6.append(count)
plt.plot(x, y1,'-o',label='domain')
plt.plot(x, y2,'-v',label='sitekey')
plt.plot(x, y3,'-^',label='third-party')
plt.plot(x, y4,'-<',label='websocket')
plt.plot(x, y5,'->',label='webrtc')
plt.plot(x, y6,'-1',label='csp')
plt.xticks(rotation='vertical')
plt.xlabel('Year')
plt.ylabel('Count')
plt.legend(ncol=2)
plt.tight_layout()
plt.savefig('easylist-property-type.pdf ', format='pdf', dpi=1200)
def plot_average(collected_results, versions, args, plot_std=True):
test_type = args.test_type
model_name = args.model
means, stds = [], []
for version in versions:
data = collected_results[version]
if (plot_std):
means.append(np.mean(data))
stds.append(np.std(data))
else:
means.append(data)
means = np.array(means)
stds = np.array(stds)
if (test_type == "size" or test_type == "allsize"):
x = ["0%", "20%", "40%", "60%", "80%", "100%"]
elif (test_type == "accdomain" or test_type == "moredomain"):
x = [0, 1, 2, 3, 4]
else:
x = versions
color = 'blue'
plt.plot(x, means, color=color)
if (plot_std):
plt.fill_between(x,
means - stds,
means + stds,
alpha=0.1,
edgecolor=color,
facecolor=color,
linewidth=1,
antialiased=True)
plt.xticks(np.arange(len(x)), x, fontsize=18)
plt.yticks(fontsize=18)
plt.xlabel(XLABELS[test_type], fontsize=18)
plt.ylabel('average absolute effect size', fontsize=18)
plt.title("Influence of {} on bias removal \nfor {}".format(
TITLES[test_type], MODEL_FORMAL_NAMES[model_name]),
fontsize=18)
plt.tight_layout()
plot_path = os.path.join(
args.eval_results_dir, "plots",
"{}-{}-avg{}.png".format(model_name, test_type,
"-std" if plot_std else ""))
plt.savefig(plot_path)
def bar_graph(category, age_grp, sex, x, y, year=None, country=None):
plt.figure()
plt.ylabel('ATE = Y1 - Y0')
plt.xlabel('Years')
plt.bar(range(len(x)), x, align='center')
plt.xticks(range(len(x)), y, rotation='vertical')
if country:
plt.title("%s Suicide Rates for WC; %s ages %s" %
(country, sex, age_grp))
name = country + sex + age_grp + '.png'
# plt.show()
plt.tight_layout()
plt.savefig('./graphs/Countries' + '/' + sex + '/' +
name.replace(' ', '_'))
elif year:
plt.title("Change in Suicide Rates per Country in %s; %s ages %s" %
(year, sex, age_grp))
name = category + sex + str(year) + age_grp + '.png'
# plt.show()
plt.tight_layout()
plt.savefig('./graphs/' + category + '/' + sex + '/' + str(year) +
'/' + name.replace(' ', ''))
else:
plt.title("Change in Suicide Rates in %s Countries; %s ages %s" %
(category, sex, age_grp))
name = category + sex + age_grp + '.png'
# plt.show()
plt.tight_layout()
plt.savefig('./graphs/' + category + '/' + sex + '/' +
name.replace(' ', ''))
plt.ylabel('K-mer density [0-1] for word '+word_hash)
avg_freq = float(frequency)/float(seq_len)
uniform = 1 / ( 4 ** len(word_hash) )
print('Average frequency distribution: ', avg_freq)
print('Expected uniform distribution: ', uniform)
print('Total accounted for: ', total)
plt.plot([0, vector_l], [avg_freq, avg_freq], color="blue", label='Average frequency distribution')
plt.plot([0, vector_l], [2*avg_freq, 2*avg_freq], color="cyan", label='Average frequency distribution')
plt.plot([0, vector_l], [uniform, uniform], color="orange", label='Uniform distribution')
plt.plot([0, vector_l], [observed_prob, observed_prob], color="green", label='Observed probability')
leg = axes.legend()
plt.savefig(filepath+'.png')
labels = [item.get_text() for item in axes.get_xticklabels()]
labels = [int(item) * seq_len/vector_l for item in labels]
labels = [ "{:.2E}".format((item)) for item in labels]
axes.set_xticklabels(labels)
plt.tight_layout()
plt.savefig(filepath+'.png')
print('\nSaved figure at ', filepath+'.png')
#plt.plot(x,y)
def multiplot_gen_content_type():
font = {'family': 'Liberation Serif',
'weight': 'normal',
'size': 15
}
# play around with the font size if it is too big or small
matplotlib.rcParams['axes.titlesize'] = 12
matplotlib.rcParams['axes.labelsize'] = 12
matplotlib.rc('font', **font)
# matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['pdf.use14corefonts'] = True
x = list(data.keys())
y1 =[]
y2 =[]
y3 =[]
y4 =[]
y5 =[]
y6 =[]
y7 =[]
y8 =[]
y9 =[]
y10 =[]
y11 =[]
y12 =[]
y13 =[]
y14 =[]
y15 =[]
print("---",y1)
for year in data.keys():
for option_name, count in data[year].items():
if option_name == 'script':
y1.append(count)
if option_name == 'xmlhttprequest':
y2.append(count)
if option_name == 'document':
y3.append(count)
if option_name == 'elemhide':
y4.append(count)
if option_name == 'subdocument':
y5.append(count)
if option_name == 'image':
y6.append(count)
if option_name == 'popup':
y7.append(count)
if option_name == 'ping':
y8.append(count)
if option_name == 'stylesheet':
y9.append(count)
if option_name == 'object':
y10.append(count)
if option_name == 'generichide':
y11.append(count)
if option_name == 'font':
y12.append(count)
if option_name == 'media':
y13.append(count)
if option_name == 'genericblock':
y14.append(count)
if option_name == 'other':
y15.append(count)
print("x-->",x)
print("y-->",y1)
print("y-->",y2)
plt.xticks(rotation='vertical')
plt.xlabel('Year')
plt.ylabel('Count')
plt.legend(ncol=2)
plt.tight_layout()
plt.savefig('easylist-content-type.pdf ', format='pdf', dpi=1200)
BergondGCs = Bergond[Bergond['Type'] =='gc']
BergondGCs[['RAJ2000', 'DEJ2000']].to_csv('/Volumes/VINCE/OAC/imaging/BergondGCs_RADEC.reg', index = False, sep =' ', header = None)
cat1 = coords.SkyCoord(GCs['RA_g'], GCs['DEC_g'], unit=(u.degree, u.degree))
cat2 = coords.SkyCoord(BergondGCs['RAJ2000'], BergondGCs['DEJ2000'], unit=(u.degree, u.degree))
index,dist2d, _ = cat1.match_to_catalog_sky(cat2)
mask = dist2d.arcsec < 0.3
new_idx = index[mask]
VIMOS = GCs.ix[mask].reset_index(drop = True)
BergondMatch = BergondGCs.ix[new_idx].reset_index(drop = True)
print len(BergondMatch)
x = VIMOS['VREL_helio']
xerr = VIMOS['VERR']
y = BergondMatch['HRV']
yerr = BergondMatch['e_HRV']
plt.errorbar(x, y, yerr= yerr, xerr = xerr, fmt = 'o', c ='red',label = 'Bergond et al.')
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.xlabel(r'Velocity from this work [km s$^-1$ ]')
plt.ylabel(r'Velocity from the literature [km s$^-1$ ]')
plt.legend(loc = 'upper left')
plt.tight_layout()
plt.show()
print 'rms (VIMOS - Bergond) GCs = ', np.std(x-y)