fig, ax=plt.subplots()
plt.plot([],[], color = 'm', label = 'Nuclear', linewidth=5)
plt.plot([],[], color = 'y', label = 'Coal', linewidth=5)
plt.plot([],[], color = 'c', label = 'NG', linewidth=5)
plt.plot([],[], color = 'r', label = 'Other', linewidth=5)
plt.plot([],[], color = 'g', label = 'PV', linewidth=5)
plt.plot([],[], color = 'b', label = 'Wind', linewidth=5)
plt.plot([],[], color = 'k', label = 'Discharge', linewidth=5)
plt.plot(X,-Over_el, label = 'Surplus', linewidth=1)
plt.plot(X,negative_el, label = 'charge', linewidth=1)
ax.stackplot(X, Nuke_el, Coal_el , NG_el , Other_el , PV_el, Wind_el, positive_el, colors=['m','y','c','r', 'g', 'b','k'])
ax.plot(X, D_el, 'r--', linewidth=3)
ax.stackplot(X, negative_el,-Over_el, colors=['y','b'])
begin=3864
end=4032
spacing = 24
minorLocator = MultipleLocator(spacing)
ax.set_xlim([begin,end])
ax.set_xlabel('hour')
ax.set_ylabel('MWh/h')
ax.xaxis.set_minor_locator(minorLocator)
plt.xticks(np.arange(begin, end, 24))
plt.grid()
plt.title('Electricity Balance')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.savefig('hourlybalance.png',bbox_inches='tight')
plt.show()
#!/usr/bin/python
#-*-coding:utf-8-*-
import sys
import numpy as np
import simtool
import matplotlib
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
matplotlib.use('Agg')
print 'matplotlib.use("Agg")'
import matplotlib.pyplot as plt
xmajorLocator = MultipleLocator(5) #将x主刻度标签设置为20的倍数
xmajorFormatter = FormatStrFormatter('%5d') #设置x轴标签文本的格式
xminorLocator = MultipleLocator(0.5) #将x轴次刻度标签设置为5的倍数
ymajorLocator = MultipleLocator(10) #将y轴主刻度标签设置为0.5的倍数
ymajorFormatter = FormatStrFormatter('%10d') #设置y轴标签文本的格式
yminorLocator = MultipleLocator(1) #将此y轴次刻度标签设置为0.1的倍数
def getbinspdfcdf(sample_list):
if len(sample_list) <= 0:
return (0, ), (0, ), (0, ),
bins = np.linspace(min(sample_list), max(sample_list), 100)
pdf = np.histogram(sample_list, bins=bins, normed=False)[0]
pdf = pdf * 1.0 / sum(pdf)
cdf = [sum(pdf[:i]) for i in xrange(1, len(pdf) + 1)]
return bins[1:], pdf, cdf
#ss: Simulation List
ax.set(xlim=(0, 5000))
ax2.set(xlim=(0, 5000))
#ax2.set(xlim=(126, 128))
#ax.spines['right'].set_visible(False)
#ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
#ax2.yaxis.tick_right()
ax.set_yscale('log')
ax.set_ylabel('Count\n(GMP)')
ax.set_xlabel('time (us)')
ax2.set_yscale('log')
ax2.set_ylabel('Count\n(libint)')
ax2.set_xlabel('time (us)')
ax.xaxis.set_minor_locator(MultipleLocator(500))
ax.tick_params(which='minor', length=8)
ax.tick_params(which='major', length=6)
ax2.xaxis.set_minor_locator(MultipleLocator(500))
ax2.tick_params(which='minor', length=8)
ax2.tick_params(which='major', length=6)
fig.set_size_inches(18.5, 10)
fig.tight_layout()
# Only edit the labels _now_
fig.canvas.draw()
labels = [item.get_text() for item in ax.get_xticklabels(which='both')]
labels[-1] = "longer"
ax.set_xticklabels(labels)
fig.canvas.draw()
freq = np.fft.rfftfreq(2**n, Ly) * 2**n
cs = ax.imshow(kl_1[0:2**(n - 5), 0:2**(n - 4)],
interpolation="none",
cmap='Spectral',
origin='lower',
extent=[0, freq[2**(n - 4)], 0, freq[2**(n - 5)]],
vmax=np.max(kl_1),
vmin=0)
ax.set(xlabel='Wavenumber y-axis [1/m]', ylabel='Wavenumber z-axis [1/m]')
ax.set_title('FFT2 [Adimentionalized field units]')
cbar = fig.colorbar(cs)
plt.subplots_adjust(left=0.14,
bottom=0.2,
right=1,
top=0.9,
wspace=0.0,
hspace=0.0)
#plt.show()
ax.set_xlim(0, freq[2**(n - 4)])
ax.set_ylim(0, freq[2**(n - 5)])
ax.xaxis.set_minor_locator(AutoMinorLocator(3))
ax.yaxis.set_minor_locator(AutoMinorLocator(3))
ax.xaxis.set_major_locator(MultipleLocator(20))
ax.yaxis.set_major_locator(MultipleLocator(20))
# Turn grid on for both major and minor ticks and style minor slightly
# differently.
ax.grid(which='major', color='#CCCCCC', linestyle='-')
ax.grid(which='minor', color='#CCCCCC', linestyle=':')
#plt.grid()
#plt.show()
plt.savefig('Poiseuille_FFT2_Vort', dpi=600)
plt.errorbar(x,
y,
xerr=0.04,
yerr=0.005,
ls="--",
marker="o",
color="k",
fmt=None)
a = np.arange(min(x) - 10, max(x) + 10, 0.001)
p = np.poly1d(np.polyfit(x, y, 1))
print('Уравнение', np.poly1d(np.polyfit(x, y, 1)))
plt.plot(a, p(a), color='k')
plt.xlim(0.7, 1.3)
plt.ylim(0.04, 0.11)
xminorLocator = MultipleLocator(0.025)
yminorLocator = MultipleLocator(0.005)
xmajorLocator = MultipleLocator(0.125)
ymajorLocator = MultipleLocator(0.025)
ax = plt.subplot()
ax.xaxis.set_minor_locator(xminorLocator)
ax.yaxis.set_minor_locator(yminorLocator)
ax.xaxis.set_major_locator(xmajorLocator)
ax.yaxis.set_major_locator(ymajorLocator)
plt.grid(which='major', ls='-', lw=0.5, c='k')
plt.grid(which='minor', ls='--', lw=0.5, c='grey')
plt.title("Рис.5: График зависимости d(1/D)")
plt.xlabel(u"1/D, 1/мм")
plt.ylabel(u"d, мм")
plt.show()
left_axis.tick_params(axis='both',
which='minor',
direction='inout',
length=minor_grid_tick_length)
#
###
#
# axis domain and range
#
plot.xlim(xmin, xmax)
left_axis.axis(ymin=ymin, ymax=ymax)
###
#
# axis ticks
#
left_axis.xaxis.set_major_locator(MultipleLocator(xmajortick))
left_axis.xaxis.set_minor_locator(MultipleLocator(xminortick))
left_axis.yaxis.set_major_locator(MultipleLocator(ymajortick))
left_axis.yaxis.set_minor_locator(MultipleLocator(yminortick))
#
###
#
# log scale option
# xmin,ymin !=0 for log scale
#
#left_axis.set_xscale('log')
left_axis.set_yscale('log')
#
###
#
# annotation
import csv
import math
from pylab import *
from matplotlib.ticker import MultipleLocator
majorGap = 20
xMajorLocator = MultipleLocator(majorGap) # 将x主刻度标签设置为20的倍数
xMinorLocator = MultipleLocator(5) # 将x轴次刻度标签设置为5的倍数
cate = 6255
brandBuyLimitNum = 500
brandList = []
brandInfoFile = "brandFeatureOfCate" + str(cate) + ".csv"
brandSrc = csv.reader(open(brandInfoFile, 'r'))
i = 0 # 跳过标题栏
for row in brandSrc:
if i == 0:
i = i + 1
continue
row[5] = float(row[5])
if row[5] >= brandBuyLimitNum:
brandList.append(row[0])
for brand in brandList:
# 寻找品牌购买前的最大浏览时间和最小浏览时间
brandMinViewTime = 0
brandMaxViewTime = 0
userInfoFile = "userFeatureOfCate" + str(cate) + ".csv"
userInfo = csv.reader(open(userInfoFile, 'r'))
def group_comp_simp(df, phases, rois=['rACC', 'sgACC']):
comp = pd.DataFrame(index=pd.MultiIndex.from_product(
[rois, phases], names=['roi', 'encode_phase']))
phase_pal = sns.color_palette(
['black', 'darkmagenta', 'lightgreen', 'seagreen'], desat=1)
for roi in rois:
for phase in phases:
c = df.loc[('healthy', roi, phase), 'rsa'].values
cci, cdist = pg.compute_bootci(c,
func='mean',
n_boot=1000,
return_dist=True,
method='cper',
decimals=3,
seed=42)
p = df.loc[('ptsd', roi, phase), 'rsa'].values
cci, pdist = pg.compute_bootci(p,
func='mean',
n_boot=1000,
return_dist=True,
method='cper',
decimals=3,
seed=42)
dif = cdist - pdist
comp.loc[(roi, phase), 'point'] = dif.mean()
# tres = pg.ttest(c,p,paired=False,correction=False)
comp.loc[(roi, phase),
'ci_l'] = np.percentile(dif, 5) #tres['CI95%'][0][0]
comp.loc[(roi, phase),
'ci_u'] = np.percentile(dif, 100) #tres['CI95%'][0][1]
comp.loc[(roi, phase),
'p'] = 1 - np.mean(dif > 0) #tres['p-val'][0]
comp.loc[roi, 'corrp'] = pg.multicomp(list(comp.loc[roi, 'p'].values),
method='fdr_bh')[1]
comp['sig'] = comp.corrp.apply(pconvert)
fig, ax = plt.subplots(1, 2, sharey=True, sharex=True, figsize=(10.5, 4.5))
for i, roi in enumerate(rois):
dat = comp.loc[roi].reset_index()
Ax = ax[i]
sns.barplot(data=dat,
y='encode_phase',
x='point',
palette=phase_pal,
ax=Ax,
seed=42,
errcolor='black',
orient='h')
# X_together = [[x-.2,x+.2] for x in Ax.get_xticks()]
# X = [x for t in X_together for x in t]
X = Ax.get_yticks()
Ax.hlines(X,
dat.ci_l.values,
dat.ci_u.values,
linewidth=5,
color='black').set_capstyle('round')
Ax.vlines(0,
Ax.get_ylim()[0],
Ax.get_ylim()[1],
color='grey',
linestyle='--',
linewidth=3)
# Ax.legend_.remove()
# Ax.set_ylim(ymin,ymax)
Ax.xaxis.set_major_locator(MultipleLocator(.1))
Ax.xaxis.set_minor_locator(MultipleLocator(.05))
sns.despine(ax=Ax, trim=True)
for i, x in enumerate(X):
Ax.annotate(comp.loc[roi, 'sig'].values[i],
xy=(x - .05, Ax.get_ylim()[1] - .1))
ax[0].set_xlabel('∆ fisher z(r)')
ax[1].set_xlabel('∆ fisher z(r)')
ax.plot(df2[col_ni], df2[col], 'k--', label='Herzbach 2005')
# for item, col, col_ni in zip(ax, col_list, col_list_ni_r):
# item.plot(df['r'],df[col],'k-',label='This Work')
# item.plot(df2[col_ni],df2[col],'k--',label='Herzbach 2005')
# ax[0].tick_params(axis='both',direction='out')
ax.tick_params(axis='both', which='major', length=6)
ax.tick_params(axis='both', which='minor', length=2)
ax.tick_params(axis='x', which='both', direction='in')
ax.legend(title=col, )
# ax.legend(prop={'weight':'normal'}, title= fr'{title}').get_frame().set_edgecolor('k')
ax.legend(prop={'weight': 'normal'}, title=fr'{col}', frameon=False)
# for item, val,val2 in zip(ax,major_tick,minor_tick):
ax.xaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(1))
ax.xaxis.set_minor_locator(MultipleLocator(0.5))
ax.yaxis.set_minor_locator(MultipleLocator(0.5))
# item.yaxis.set_major_formatter(FormatStrFormatter('%d'))
ax.set_xlim([3, 6])
ax.set_ylim([0, 3])
# ax[1].set_ylim([0,3.5])
# ax[2].set_ylim([0,1.5])
# ax[2].set_xlabel(r'$\bf{r(}$Å$\bf{)}$',fontweight='normal')
ax.set_xlabel(r'r(Å)', fontweight='normal')
ax.set_ylabel(r'g$_{ij}$(r)', fontweight='normal')
# ax[1].set_ylabel('g(r)',fontweight='bold')
# For common y axis title
def main():
# Plot VLE envelopes
clrs = seaborn.color_palette('bright', n_colors=len(df))
np.random.seed(11)
np.random.shuffle(clrs)
fig, ax = plt.subplots(figsize=(8, 6))
temps = R125.expt_liq_density.keys()
for temp in temps:
ax.scatter(
df.filter(regex=(f"liq_density_{float(temp):.0f}K")),
np.tile(temp, len(df)),
c=clrs,
s=160,
alpha=0.2,
)
ax.scatter(
df.filter(regex=(f"vap_density_{float(temp):.0f}K")),
np.tile(temp, len(df)),
c=clrs,
s=160,
alpha=0.2,
)
ax.scatter(
df.filter(regex=("sim_rhoc")),
df.filter(regex=("sim_Tc")),
c=clrs,
s=160,
alpha=0.2,
)
tc, rhoc = calc_critical(df_gaff)
ax.scatter(
df_gaff["rholiq_kgm3"],
df_gaff["T_K"],
c='gray',
s=120,
alpha=0.7,
label="GAFF",
marker='s',
)
ax.scatter(
df_gaff["rhovap_kgm3"],
df_gaff["T_K"],
c='gray',
s=120,
alpha=0.7,
marker='s',
)
ax.scatter(
rhoc,
tc,
c='gray',
s=120,
alpha=0.7,
marker='s',
)
ax.scatter(
R125.expt_liq_density.values(),
R125.expt_liq_density.keys(),
color="black",
marker="x",
linewidths=3,
s=300,
label="Experiment",
zorder=-1,
)
ax.scatter(
R125.expt_vap_density.values(),
R125.expt_vap_density.keys(),
color="black",
marker="x",
linewidths=3,
s=300,
zorder=-1,
)
ax.scatter(R125.expt_rhoc,
R125.expt_Tc,
color="black",
marker="x",
linewidths=3,
s=300,
zorder=-1)
ax.set_xlim(-100, 1550)
ax.xaxis.set_major_locator(MultipleLocator(400))
ax.xaxis.set_minor_locator(AutoMinorLocator(4))
ax.set_ylim(220, 380)
ax.yaxis.set_major_locator(MultipleLocator(40))
ax.yaxis.set_minor_locator(AutoMinorLocator(4))
ax.tick_params("both",
direction="in",
which="both",
length=4,
labelsize=26,
pad=10)
ax.tick_params("both", which="major", length=8)
ax.xaxis.set_ticks_position("both")
ax.yaxis.set_ticks_position("both")
ax.set_ylabel("T (K)", fontsize=32, labelpad=15)
ax.set_xlabel(r"$\mathregular{\rho}$ (kg/m$\mathregular{^3}$)",
fontsize=32,
labelpad=12)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2.0)
ax.legend(loc="lower left",
bbox_to_anchor=(0.1, 1.0),
ncol=3,
fontsize=20,
handletextpad=0.1,
markerscale=0.8)
ax.text(0.6, 0.8, "HFC-125", fontsize=34, transform=ax.transAxes)
fig.subplots_adjust(bottom=0.3, top=0.85, left=0.25, right=0.95)
fig.savefig("ffo_pdfs/vle-r125.pdf")
# Plot Vapor Pressure
fig, ax = plt.subplots(figsize=(8, 6))
for temp in temps:
ax.scatter(
1000 / np.tile(temp, len(df)),
df.filter(regex=(f"Pvap_{float(temp):.0f}K")),
c=clrs,
s=160,
alpha=0.2,
)
ax.scatter(
1000 / df_gaff["T_K"],
df_gaff["pvap_bar"],
c='gray',
s=120,
alpha=0.7,
label="GAFF",
marker='s',
)
ax.scatter(
1000 / np.array(list(R125.expt_Pvap.keys())),
R125.expt_Pvap.values(),
color="black",
marker="x",
linewidths=3,
s=300,
label="Experiment",
zorder=-1,
)
ax.set_xlim(2.9, 4.3)
ax.xaxis.set_major_locator(MultipleLocator(0.3))
ax.xaxis.set_minor_locator(AutoMinorLocator(3))
ax.set_ylim(1, 100)
#ax.yaxis.set_major_locator(MultipleLocator(10))
#ax.yaxis.set_minor_locator(AutoMinorLocator(5))
ax.tick_params("both",
direction="in",
which="both",
length=4,
labelsize=26,
pad=10)
ax.tick_params("both", which="major", length=8)
ax.xaxis.set_ticks_position("both")
ax.yaxis.set_ticks_position("both")
ax.set_yscale("log")
ax.set_xlabel(r"1000 / T (K$^{-1}$)", fontsize=32, labelpad=20)
ax.set_ylabel(r"$\mathregular{P_{vap}}$ (bar)", fontsize=32, labelpad=20)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2.0)
ax.legend(loc="lower left",
bbox_to_anchor=(0.1, 1.05),
ncol=3,
fontsize=20,
handletextpad=0.1,
markerscale=0.8)
ax.text(0.6, 0.8, "HFC-125", fontsize=34, transform=ax.transAxes)
fig.subplots_adjust(bottom=0.3, top=0.85, left=0.25, right=0.95)
fig.savefig("ffo_pdfs/pvap-r125.pdf")
# Plot Enthalpy of Vaporization
fig, ax = plt.subplots(figsize=(8, 6))
for temp in temps:
ax.scatter(
np.tile(temp, len(df)),
df.filter(regex=(f"Hvap_{float(temp):.0f}K")),
c=clrs,
s=160,
alpha=0.2,
)
ax.scatter(
df_gaff["T_K"],
df_gaff["hvap_kJmol"] / R125.molecular_weight * 1000.0,
c='gray',
s=120,
alpha=0.7,
label="GAFF",
marker='s',
)
ax.scatter(
R125.expt_Hvap.keys(),
R125.expt_Hvap.values(),
color="black",
marker="x",
linewidths=3,
s=300,
label="Experiment",
zorder=-1,
)
ax.set_xlim(220, 330)
ax.xaxis.set_major_locator(MultipleLocator(30))
ax.xaxis.set_minor_locator(AutoMinorLocator(3))
ax.set_ylim(-10, 410)
ax.yaxis.set_major_locator(MultipleLocator(100))
ax.yaxis.set_minor_locator(AutoMinorLocator(5))
ax.tick_params("both",
direction="in",
which="both",
length=4,
labelsize=26,
pad=10)
ax.tick_params("both", which="major", length=8)
ax.xaxis.set_ticks_position("both")
ax.yaxis.set_ticks_position("both")
ax.set_xlabel("T (K)", fontsize=32, labelpad=20)
ax.set_ylabel(r"$\mathregular{\Delta H_{vap}}$ (kJ/kg)",
fontsize=32,
labelpad=20)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2.0)
ax.legend(loc="lower left",
bbox_to_anchor=(0.1, 1.0),
ncol=3,
fontsize=20,
handletextpad=0.1,
markerscale=0.8)
ax.text(0.6, 0.8, "HFC-125", fontsize=34, transform=ax.transAxes)
fig.subplots_adjust(bottom=0.3, top=0.85, left=0.25, right=0.95)
fig.savefig("ffo_pdfs/hvap-r125.pdf")
def plot_fit(self, fit):
fmbinlog, axmbinlog = plt.subplots(figsize=(12, 7))
chain = fit.extract()
# Plot the points from above as a comparison
x_model = np.linspace(6.5, 11, 100)
for counter, c in enumerate(self.sampled_rates.columns):
label = None
if counter == 0:
label = 'Observations'
axmbinlog.scatter(self.sampled_rates.index,
self.sampled_rates[c],
color='g',
marker='o',
alpha=0.05,
s=10,
label=label)
axmbinlog.xaxis.set_minor_locator(MultipleLocator(0.25))
axmbinlog.yaxis.set_minor_locator(MultipleLocator(0.125))
axmbinlog.tick_params(which='both',
right=True,
top=True,
direction='in',
labelsize=16)
axmbinlog.set_xlabel('Stellar Mass $\log (M_*/M_{\odot})$',
size=20)
axmbinlog.set_ylabel(
'$\log (N$ (SN hosts) / $N$ (Field Galaxies) )', size=20)
for i in self.sampled_rates.index:
axmbinlog.errorbar(i,
self.sampled_rates.loc[i].mean(),
xerr=0.184,
color='g',
marker='D',
alpha=0.5,
markersize=2,
mew=0.5,
mec='w')
level = 95
axmbinlog.fill_between(x_model,
np.percentile(chain['line'],
50 - 0.5 * level,
axis=0),
np.percentile(chain['line'],
50 + 0.5 * level,
axis=0),
color='c',
alpha=0.2)
level = 68
axmbinlog.fill_between(x_model,
np.percentile(chain['line'],
50 - 0.5 * level,
axis=0),
np.percentile(chain['line'],
50 + 0.5 * level,
axis=0),
color='c',
alpha=0.3)
axmbinlog.plot(x_model,
np.percentile(chain['line'], 50, axis=0),
color='b',
alpha=1,
linestyle='-',
linewidth=1,
label='$dN/dG = %.2f$' % np.median(chain['slope']))
leg = axmbinlog.legend()
for lh in leg.legendHandles:
lh.set_alpha(1)
plt.savefig(self.root_dir + 'figs/rate_vs_mass_slopes_stanfit.png')
dijet = dijet[:, 9:]
bbjet = bbjet[:, 9:]
#ttbar=ttbar[:, 5:]
weights = f['X_weights'][:]
training_weights = f['X_weights_train'][:]
testing_weights = f['X_weights_test'][:]
val_weights = f['X_weights_val'][:]
variablelist = variablelist[9:]
#fig, ax= plt.subplots(34, 3, figsize=(40,280))
#fig, ax = plt.subplots()
nbins = 50
varcounter = -1
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
xmajorLocator = MultipleLocator(200)
xmajorFormatter = FormatStrFormatter('%1d')
xminorLocator = MultipleLocator(50)
ymajorLocator = MultipleLocator(2000)
ymajorFormatter = FormatStrFormatter('%1d')
yminorLocator = MultipleLocator(1e1)
for varcounter in range(95):
print(variablelist[varcounter])
if variablelist[varcounter] == "jet_pt" or variablelist[
varcounter] == "jet_m":
nbins = 100
plt.hist(bbjet[:, varcounter] / 1e3,
nbins,
density=0,
weights=bb_vs_bb_weights,
elif name == 'F44':
index2 = 6
scattering_elements[name] = data
pylab.subplot(4, 4, index + 1)
letter, number = split_name(name)
if index == 0:
pylab.yscale('log')
pylab.text(30, 0.03, name, fontsize=12)
elif index2 == 3:
pylab.ylim(ymin=0)
pylab.ylim(ymax=1.1)
pylab.gca().yaxis.set_minor_locator(MultipleLocator(0.1))
pylab.text(15, 0.7, name + r'$\rm /F_{11}$', fontsize=12)
else:
pylab.ylim(ymin=-1)
pylab.ylim(ymax=1)
pylab.gca().yaxis.set_minor_locator(MultipleLocator(0.1))
if index == 1:
pylab.text(15, -0.7, '-' + name + r'$\rm /F_{11}$', fontsize=12)
else:
pylab.text(15, -0.7, name + r'$\rm /F_{11}$', fontsize=12)
if index == 1:
pylab.plot(data['deg'],
-data['data'],
"o",
if value < 0 or value > len(days):
return ""
origin = datetime.date(year = 1900, month = 1, day = 1)
delta = datetime.timedelta(days = int(days[int(value)]) - 2)
date = origin + delta
return str(date.day) + "/" + str(date.month)
fig, (ax1, ax2) = plt.subplots(2, figsize=(15, 8), gridspec_kw={'height_ratios': [1, 2]})
final_reward = np.around(rewards[-1], decimals = 4)
ax1.plot(rewards)
ax1.set_title("Cumulative sum of rewards - " + filename + " (final reward: " + str(final_reward) + ")")
ax1.set(xlabel = "Date", ylabel = "Cumulative reward")
ax1.set_xbound(lower=0, upper=len(days))
ax1.xaxis.set_major_locator(MultipleLocator(50))
ax1.xaxis.set_major_formatter(FuncFormatter(format_date))
ax1.yaxis.set_major_locator(MaxNLocator(5))
ax1.grid()
plot = ax2.imshow(actions, cmap='GnBu')
ax2.set_title("Actions heatmap - " + filename)
ax2.set(xlabel = "Time of day", ylabel = "Date")
ax2.xaxis.set_major_locator(MultipleLocator(300))
ax2.xaxis.set_major_formatter(FuncFormatter(format_time))
ax2.yaxis.set_major_locator(MultipleLocator(50))
ax2.yaxis.set_major_formatter(FuncFormatter(format_date))
fig.colorbar(plot, ticks=[-1, 0, 1], fraction=0.0109, pad=0.01)
plt.savefig("visualize-actions/" + filename + ".png", format='png')
def plot_exp_fatigue_life(figure_path=None, figure_name=None, save_types=[]):
#==============================================================================
# title
#==============================================================================
title = ''
#==============================================================================
# figure format
# http://matplotlib.org/users/customizing.html?highlight=rcparams
#==============================================================================
plot_format()
#==============================================================================
# grid set up
#==============================================================================
#plt.grid(True, which='major',linestyle='-')
#plt.grid(True, which='minor',linestyle='-')
#plt.grid(True, which='major')
#plt.grid(True, which='minor')
#==============================================================================
# print title
#==============================================================================
plt.title(title, fontsize=16)
#==============================================================================
# x,y limite
#==============================================================================
plt.xlim(1E0, 1E5)
plt.ylim(0.3, 1.0)
#==============================================================================
# xy log scale
#==============================================================================
plt.xscale('log')
plt.yscale('log')
#==============================================================================
# xy axial equal
#==============================================================================
ax = plt.gca()
# ax.set_aspect('equal')
ax.set_aspect('auto')
#==============================================================================
# http://stackoverflow.com/questions/21920233/matplotlib-log-scale-tick-label-number-formatting
#==============================================================================
ax.yaxis.set_major_locator(MultipleLocator(0.1))
ax.yaxis.set_minor_locator(MultipleLocator(0.01))
ax.yaxis.set_major_formatter(ScalarFormatter())
#==============================================================================
# plot lines
#==============================================================================
plot_data = PlotData()
plot_data.readFromFile(figure_path, figure_name)
plot_data.plot()
#==============================================================================
# show legend
#==============================================================================
plt.legend(loc=1, frameon=True)
# plt.legend(loc=0,fontsize='small',frameon=True,numpoints=1,title='Temperature')
#==============================================================================
# save figures
#==============================================================================
if figure_path <> None and figure_name <> None:
for save_type in save_types:
plt.savefig(figure_path + figure_name + save_type,
dpi=150,
transparent=True)
print 'save as', figure_path + figure_name + save_type
plt.show()
plt.close()
def split_level(df, group, rois=['rSMA', 'sgACC'], phases=None, split=None):
if split == 'level':
splits = ['item', 'set']
# pal = sns.palettes.color_palette('Set2',n_colors=2)
pal = ['blue', 'lightblue']
elif split == 'memory_phase':
splits = ['encoding', 'retrieval']
# pal = sns.palettes.color_palette('Set2',n_colors=4)[2:]
pal = ['firebrick', 'salmon']
df = df.loc[group]
out = {}
for val in ['ci', 'dist']:
out[val] = {}
for roi in rois:
out['ci'][roi] = {}
out['dist'][roi] = {}
for phase in phases:
out['ci'][roi][phase] = {}
out['dist'][roi][phase] = {}
for level in splits:
out['ci'][roi][phase][level] = {}
out['dist'][roi][phase][level] = {}
out['ci'][roi][phase][level], out['dist'][roi][phase][
level] = pg.compute_bootci(df.loc[(roi, phase, level),
'rsa'].values,
func='mean',
n_boot=10000,
return_dist=True,
method='cper',
decimals=3,
seed=42)
ci = pd.DataFrame.from_dict(
out['ci'],
orient='index').reset_index().rename(columns={'index': 'roi'})
ci = ci.melt(id_vars='roi', var_name='encode_phase',
value_name='ci').set_index(['roi',
'encode_phase']).sort_index()
ci = ci.ci.apply(pd.Series).reset_index()
ci = ci.melt(id_vars=['roi', 'encode_phase'],
var_name='level',
value_name='ci').set_index(['roi', 'encode_phase', 'level'])
dist = pd.DataFrame.from_dict(
out['dist'],
orient='index').reset_index().rename(columns={'index': 'roi'})
dist = dist.melt(id_vars='roi', var_name='encode_phase',
value_name='dist').set_index(['roi', 'encode_phase'])
dist = dist.dist.apply(pd.Series).reset_index()
dist = dist.melt(id_vars=['roi', 'encode_phase'],
var_name='level',
value_name='dist').set_index(
['roi', 'encode_phase', 'level'])
dist = dist.dist.apply(pd.Series).stack().reset_index(-1, drop=True)
dist = dist.reset_index().rename(columns={0: 'dist'})
ci['point'] = dist.groupby(['roi', 'encode_phase', 'level']).mean()
ci = ci.reset_index()
ci.roi = pd.Categorical(ci.roi, rois, ordered=True)
ci.encode_phase = pd.Categorical(ci.encode_phase, phases, ordered=True)
ci.level = pd.Categorical(ci.level, splits, ordered=True)
ci = ci.sort_values(by=['encode_phase', 'level'])
ci = ci.set_index('roi').sort_index()
dist.roi = pd.Categorical(dist.roi, rois, ordered=True)
dist.encode_phase = pd.Categorical(dist.encode_phase, phases, ordered=True)
dist.level = pd.Categorical(dist.level, splits, ordered=True)
dist = dist.sort_values(by=['encode_phase', 'level'])
dist = dist.set_index('roi').sort_index()
#collect 2 tailed p-values
# pvals = dist.reset_index().groupby(['roi','encode_phase','level']
# ).apply(lambda x : (1-np.mean(x>0))/2
# ).reset_index().set_index('roi').rename(columns={'dist':'p'})
# for roi in rois: pvals.loc[roi,'corrp'] = pg.multicomp(pvals.loc[roi,'p'].values,method='fdr_bh')[1]
pvals = pd.DataFrame(columns=['p'],
index=pd.MultiIndex.from_product(
[rois, phases, splits],
names=['roi', 'encode_phase', 'level']))
for roi in rois:
for phase in phases:
for s in splits:
pvals.loc[(roi, phase, s), 'p'] = pg.ttest(
df.loc[(roi, phase, s), 'rsa'].values,
0)['p-val'].values[0]
# for roi in rois: pvals.loc[roi,'corrp'] = pg.multicomp(list(pvals.loc[roi,'p'].values),method='fdr_bh')[1]
pvals['corrp'] = pg.multicomp(list(pvals.p.values), method='fdr_bh')[1]
pvals['sig'] = pvals.corrp.apply(pconvert)
phase_pal = sns.color_palette(
['black', 'darkmagenta', 'lightgreen', 'seagreen'], desat=.75)
ymax = dist.dist.max() + .05
ymin = dist.dist.min() - .05
fig, ax = plt.subplots(1, 2, sharey=True, figsize=(8, 3))
for i, roi in enumerate(rois):
dat = df.loc[roi].reset_index()
Ax = ax[i]
'''
#dist violins
sns.violinplot(data=dist.loc[roi],x='encode_phase',y='dist',cut=0,
inner=None,ax=Ax,scale='count',hue='level',split=True,palette=pal)#,palette=['white','white'])
'''
sns.barplot(data=dat,
x='encode_phase',
y='rsa',
hue=split,
palette=pal,
ax=Ax,
order=phases,
seed=42,
errcolor='black')
change_width(Ax, .35)
'''
sns.violinplot(data=dat,x='encode_phase',y='rsa',hue=split,
split=True,palette=pal,cut=0,ax=Ax,inner=None)
'''
'''
#jointplot
sns.pointplot(data=dat,x='encode_phase',y='rsa',hue=split,
dodge=.3,n_boot=1000,seed=42,markers=['o','D'],ax=Ax,palette=pal,
join=False)
'''
lower = ci.loc[roi, 'ci'].apply(lambda x: x[0])
upper = ci.loc[roi, 'ci'].apply(lambda x: x[1])
Y = ci.loc[roi, 'point']
X_together = [[x - .1, x + .1] for x in Ax.get_xticks()]
X = [x for t in X_together for x in t]
# Ax.vlines(X,lower,upper,linewidth=3,color='black').set_capstyle('butt')
# Ax.scatter(X,lower,s=240,marker='_',color='black',linewidths=5)
# Ax.scatter(X,upper,s=240,marker='_',color='black',linewidths=5)
# Ax.scatter(X,Y,s=480,marker='_',color='black',edgecolors='black') #set level
# #stripplots
# sns.stripplot(data=dat,x='encode_phase',y='rsa',hue=split,palette=pal,
# ax=Ax,dodge=.3,order=phases,alpha=.3,edgecolor='black',linewidth=1)
'''
#chicken scratch
dat = dat.set_index(['encode_phase',split])
for p, phase in enumerate(phases):
left = dat.loc[(phase,splits[0]),'rsa']
right = dat.loc[(phase,splits[1]),'rsa']
for xy in range(left.shape[0]):
xvals = X_together[p]
xvals[0] -= .01
xvals[1] += .01
Ax.plot(X_together[p],[left[xy],right[xy]],color='grey',lw=1,alpha=.5)
'''
Ax.hlines(0,
Ax.get_xlim()[0],
Ax.get_xlim()[1],
color='grey',
linestyle='--',
linewidth=3)
Ax.set_xticklabels('', rotation=45)
Ax.set_title(roi + '_' + group + '_' + split)
Ax.legend_.remove()
Ax.set_ylim(ymin, ymax)
Ax.yaxis.set_major_locator(MultipleLocator(.2))
Ax.yaxis.set_minor_locator(MultipleLocator(.1))
sns.despine(ax=Ax, trim=True)
for i, x in enumerate(X):
Ax.annotate(pvals.loc[roi, 'sig'].values[i],
xy=(x - .05, Ax.get_ylim()[1] - .1))
if len(rois) > 1:
ax[0].set_ylabel('∆ fisher z(r)')
ax[1].set_ylabel('')
else:
ax.set_ylabel('∆ fisher z(r)')
ax.set_ylabel('')
plt.savefig('./plots/%s_%s.eps' % (group, split), format='eps')
def pollyxt_dwd_display_saturation(tmpFile, saveFolder):
"""
Description
-----------
Display the housekeeping data from laserlogbook file.
Parameters
----------
tmpFile: str
the .mat file which stores the housekeeping data.
saveFolder: str
Usage
-----
pollyxt_dwd_display_saturation(tmpFile)
History
-------
2019-01-10. First edition by Zhenping
"""
if not os.path.exists(tmpFile):
print('{filename} does not exists.'.format(filename=tmpFile))
return
# read matlab .mat data
try:
mat = spio.loadmat(tmpFile, struct_as_record=True)
figDPI = mat['figDPI'][0][0]
mTime = mat['time'][0][:]
height = mat['height'][0][:]
SAT_FR_355 = mat['SAT_FR_355'][:]
SAT_FR_532 = mat['SAT_FR_532'][:]
SAT_FR_1064 = mat['SAT_FR_1064'][:]
SAT_NR_532 = mat['SAT_NR_532'][:]
yLim_FR_RCS = mat['yLim_FR_RCS'][:][0]
yLim_NR_RCS = mat['yLim_NR_RCS'][:][0]
pollyVersion = mat['campaignInfo']['name'][0][0][0]
location = mat['campaignInfo']['location'][0][0][0]
version = mat['processInfo']['programVersion'][0][0][0]
fontname = mat['processInfo']['fontname'][0][0][0]
dataFilename = mat['taskInfo']['dataFilename'][0][0][0]
xtick = mat['xtick'][0][:]
xticklabel = mat['xtickstr']
imgFormat = mat['imgFormat'][:][0]
except Exception as e:
print(e)
print('Failed reading %s' % (tmpFile))
return
# set the default font
matplotlib.rcParams['font.sans-serif'] = fontname
matplotlib.rcParams['font.family'] = "sans-serif"
# meshgrid
Time, Height = np.meshgrid(mTime, height)
# load colormap
dirname = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(dirname)
try:
from python_colormap import signal_status_colormap
except Exception as e:
raise ImportError('python_colormap module is necessary.')
# display status of 355 FR
fig = plt.figure(figsize=[10, 5])
ax = fig.add_axes([0.11, 0.15, 0.74, 0.75])
pcmesh = ax.pcolormesh(Time,
Height,
SAT_FR_355,
vmin=-0.5,
vmax=2.5,
cmap=signal_status_colormap(),
rasterized=True)
ax.set_xlabel('UTC', fontsize=15)
ax.set_ylabel('Height (m)', fontsize=15)
ax.set_ylim(yLim_FR_RCS.tolist())
ax.yaxis.set_major_locator(MultipleLocator(2500))
ax.yaxis.set_minor_locator(MultipleLocator(500))
ax.set_xticks(xtick.tolist())
ax.set_xticklabels(celltolist(xticklabel))
ax.tick_params(axis='both',
which='major',
labelsize=15,
right=True,
top=True,
width=2,
length=5)
ax.tick_params(axis='both',
which='minor',
width=1.5,
length=3.5,
right=True,
top=True)
ax.set_title('Signal Status at ' +
'{wave}nm Far-Range from {instrument} at {location}'.format(
wave=355, instrument=pollyVersion, location=location),
fontsize=15)
cb_ax = fig.add_axes([0.865, 0.15, 0.02, 0.75])
cbar = fig.colorbar(pcmesh,
cax=cb_ax,
ticks=[0, 1, 2],
orientation='vertical')
cbar.ax.tick_params(direction='in', pad=5)
cbar.ax.set_title('', fontsize=9)
cbar.ax.set_yticklabels(['Good Signal', 'Saturated', 'Low SNR'])
cbar.ax.tick_params(axis='both',
which='major',
labelsize=12,
right=True,
top=True,
width=2,
length=5)
cbar.ax.tick_params(axis='both',
which='minor',
width=1.5,
length=3.5,
right=True,
top=True)
fig.text(0.05,
0.04,
datenum_to_datetime(mTime[0]).strftime("%Y-%m-%d"),
fontsize=15)
fig.text(0.8,
0.04,
'Version: {version}'.format(version=version),
fontsize=14)
fig.savefig(os.path.join(
saveFolder, '{dataFilename}_SAT_FR_355.{imgFmt}'.format(
dataFilename=rmext(dataFilename), imgFmt=imgFormat)),
dpi=figDPI)
plt.close()
# display status of 532 FR
fig = plt.figure(figsize=[10, 5])
ax = fig.add_axes([0.11, 0.15, 0.74, 0.75])
pcmesh = ax.pcolormesh(Time,
Height,
SAT_FR_532,
vmin=-0.5,
vmax=2.5,
cmap=signal_status_colormap(),
rasterized=True)
ax.set_xlabel('UTC', fontsize=15)
ax.set_ylabel('Height (m)', fontsize=15)
ax.set_ylim(yLim_FR_RCS.tolist())
ax.yaxis.set_major_locator(MultipleLocator(2500))
ax.yaxis.set_minor_locator(MultipleLocator(500))
ax.set_xticks(xtick.tolist())
ax.set_xticklabels(celltolist(xticklabel))
ax.tick_params(axis='both',
which='major',
labelsize=15,
right=True,
top=True,
width=2,
length=5)
ax.tick_params(axis='both',
which='minor',
width=1.5,
length=3.5,
right=True,
top=True)
ax.set_title('Signal Status at ' +
'{wave}nm Far-Range from {instrument} at {location}'.format(
wave=532, instrument=pollyVersion, location=location),
fontsize=15)
cb_ax = fig.add_axes([0.865, 0.15, 0.02, 0.75])
cbar = fig.colorbar(pcmesh,
cax=cb_ax,
ticks=[0, 1, 2],
orientation='vertical')
cbar.ax.tick_params(direction='in', pad=5)
cbar.ax.set_title('', fontsize=9)
cbar.ax.set_yticklabels(['Good Signal', 'Saturated', 'Low SNR'])
cbar.ax.tick_params(axis='both',
which='major',
labelsize=12,
right=True,
top=True,
width=2,
length=5)
cbar.ax.tick_params(axis='both',
which='minor',
width=1.5,
length=3.5,
right=True,
top=True)
fig.text(0.05,
0.04,
datenum_to_datetime(mTime[0]).strftime("%Y-%m-%d"),
fontsize=15)
fig.text(0.8,
0.04,
'Version: {version}'.format(version=version),
fontsize=14)
fig.savefig(os.path.join(
saveFolder, '{dataFilename}_SAT_FR_532.{imgFmt}'.format(
dataFilename=rmext(dataFilename), imgFmt=imgFormat)),
dpi=figDPI)
plt.close()
# display status of 1064 FR
fig = plt.figure(figsize=[10, 5])
ax = fig.add_axes([0.11, 0.15, 0.74, 0.75])
pcmesh = ax.pcolormesh(Time,
Height,
SAT_FR_1064,
vmin=-0.5,
vmax=2.5,
cmap=signal_status_colormap(),
rasterized=True)
ax.set_xlabel('UTC', fontsize=15)
ax.set_ylabel('Height (m)', fontsize=15)
ax.set_ylim(yLim_FR_RCS.tolist())
ax.yaxis.set_major_locator(MultipleLocator(2500))
ax.yaxis.set_minor_locator(MultipleLocator(500))
ax.set_xticks(xtick.tolist())
ax.set_xticklabels(celltolist(xticklabel))
ax.tick_params(axis='both',
which='major',
labelsize=15,
right=True,
top=True,
width=2,
length=5)
ax.tick_params(axis='both',
which='minor',
width=1.5,
length=3.5,
right=True,
top=True)
ax.set_title('Signal Status at ' +
'{wave}nm Far-Range from {instrument} at {location}'.format(
wave=1064, instrument=pollyVersion, location=location),
fontsize=15)
cb_ax = fig.add_axes([0.865, 0.15, 0.02, 0.75])
cbar = fig.colorbar(pcmesh,
cax=cb_ax,
ticks=[0, 1, 2],
orientation='vertical')
cbar.ax.tick_params(direction='in', pad=5)
cbar.ax.set_title('', fontsize=9)
cbar.ax.set_yticklabels(['Good Signal', 'Saturated', 'Low SNR'])
cbar.ax.tick_params(axis='both',
which='major',
labelsize=12,
right=True,
top=True,
width=2,
length=5)
cbar.ax.tick_params(axis='both',
which='minor',
width=1.5,
length=3.5,
right=True,
top=True)
fig.text(0.05,
0.04,
datenum_to_datetime(mTime[0]).strftime("%Y-%m-%d"),
fontsize=15)
fig.text(0.8,
0.04,
'Version: {version}'.format(version=version),
fontsize=14)
fig.savefig(os.path.join(
saveFolder, '{dataFilename}_SAT_FR_1064.{imgFmt}'.format(
dataFilename=rmext(dataFilename), imgFmt=imgFormat)),
dpi=figDPI)
plt.close()
# display status of 532 NR
fig = plt.figure(figsize=[10, 5])
ax = fig.add_axes([0.11, 0.15, 0.74, 0.75])
pcmesh = ax.pcolormesh(Time,
Height,
SAT_NR_532,
vmin=-0.5,
vmax=2.5,
cmap=signal_status_colormap(),
rasterized=True)
ax.set_xlabel('UTC', fontsize=15)
ax.set_ylabel('Height (m)', fontsize=15)
ax.set_ylim(yLim_NR_RCS.tolist())
ax.yaxis.set_major_locator(MultipleLocator(2500))
ax.yaxis.set_minor_locator(MultipleLocator(500))
ax.set_xticks(xtick.tolist())
ax.set_xticklabels(celltolist(xticklabel))
ax.tick_params(axis='both',
which='major',
labelsize=15,
right=True,
top=True,
width=2,
length=5)
ax.tick_params(axis='both',
which='minor',
width=1.5,
length=3.5,
right=True,
top=True)
ax.set_title('Signal Status at ' +
'{wave}nm Near-Range from {instrument} at {location}'.format(
wave=532, instrument=pollyVersion, location=location),
fontsize=15)
cb_ax = fig.add_axes([0.865, 0.15, 0.02, 0.75])
cbar = fig.colorbar(pcmesh,
cax=cb_ax,
ticks=[0, 1, 2],
orientation='vertical')
cbar.ax.tick_params(direction='in', pad=5)
cbar.ax.set_title('', fontsize=9)
cbar.ax.set_yticklabels(['Good Signal', 'Saturated', 'Low SNR'])
cbar.ax.tick_params(axis='both',
which='major',
labelsize=12,
right=True,
top=True,
width=2,
length=5)
cbar.ax.tick_params(axis='both',
which='minor',
width=1.5,
length=3.5,
right=True,
top=True)
fig.text(0.05,
0.04,
datenum_to_datetime(mTime[0]).strftime("%Y-%m-%d"),
fontsize=15)
fig.text(0.8,
0.04,
'Version: {version}'.format(version=version),
fontsize=14)
fig.savefig(os.path.join(
saveFolder, '{dataFilename}_SAT_NR_532.{imgFmt}'.format(
dataFilename=rmext(dataFilename), imgFmt=imgFormat)),
dpi=figDPI)
plt.close()
def group_comp(df, phases, split):
if split == 'level':
splits = ['item', 'set']
# pal = sns.palettes.color_palette('Set2',n_colors=2)
pal = ['blue', 'lightblue']
elif split == 'memory_phase':
splits = ['encoding', 'retrieval']
# pal = sns.palettes.color_palette('Set2',n_colors=4)[2:]
pal = ['firebrick', 'salmon']
rois = ['rSMA', 'sgACC']
comp = pd.DataFrame(index=pd.MultiIndex.from_product(
[rois, phases, splits], names=['roi', 'encode_phase', split]))
for roi in rois:
for phase in phases:
for level in splits:
c = df.loc[('control', roi, phase, level), 'rsa'].values
p = df.loc[('ptsd', roi, phase, level), 'rsa'].values
comp.loc[(roi, phase, level), 'point'] = c.mean() - p.mean()
tres = pg.ttest(c, p, paired=False, correction=False)
comp.loc[(roi, phase, level), 'ci_l'] = tres['CI95%'][0][0]
comp.loc[(roi, phase, level), 'ci_u'] = tres['CI95%'][0][1]
comp.loc[(roi, phase, level), 'p'] = tres['p-val'][0]
for roi in rois:
comp.loc[roi, 'corrp'] = pg.multicomp(list(comp.loc[roi, 'p'].values),
method='fdr_bh')[1]
comp['sig'] = comp.corrp.apply(pconvert)
ymax = comp.ci_u.max() + .05
ymin = comp.ci_l.min() - .05
fig, ax = plt.subplots(1, 2, sharey=True, figsize=(24, 12))
for i, roi in enumerate(rois):
dat = comp.loc[roi].reset_index()
Ax = ax[i]
sns.barplot(data=dat,
x='encode_phase',
y='point',
hue=split,
palette=pal,
ax=Ax,
order=phases,
seed=42,
errcolor='black')
X_together = [[x - .2, x + .2] for x in Ax.get_xticks()]
X = [x for t in X_together for x in t]
Ax.vlines(X,
dat.ci_l.values,
dat.ci_u.values,
linewidth=5,
color='black').set_capstyle('round')
Ax.hlines(0,
Ax.get_xlim()[0],
Ax.get_xlim()[1],
color='grey',
linestyle='--',
linewidth=3)
Ax.legend_.remove()
Ax.set_ylim(ymin, ymax)
Ax.yaxis.set_major_locator(MultipleLocator(.1))
Ax.yaxis.set_minor_locator(MultipleLocator(.05))
sns.despine(ax=Ax, trim=True)
for i, x in enumerate(X):
Ax.annotate(comp.loc[roi, 'sig'].values[i],
xy=(x - .05, Ax.get_ylim()[1] - .1))
if len(rois) > 1:
ax[0].set_ylabel('∆ fisher z(r)')
ax[1].set_ylabel('')
else:
ax.set_ylabel('∆ fisher z(r)')
ax.set_ylabel('')
plt.savefig('./plots/group_comp_%s.eps' % (split), format='eps')
def plot_mcd(dic,
op='avg',
x_axis='Energy (eV)',
title='[PH]',
xdata='energy',
ydata='mdeg'):
plt.clf()
fig, ax = plt.subplots(figsize=(4, 2))
# norm=plt.Normalize(-10,10) #optional to remove discrete H bar divisions
norm = colors.BoundaryNorm(np.linspace(-10, 10, 11), ncolors=256)
sm = plt.cm.ScalarMappable(cmap='coolwarm_r', norm=norm)
fig.colorbar(sm, ticks=range(-10, 11, 2),
label='H (T)') #make color bar based on H (T) for plot
for df in dic.values():
#Dr. Seaborn or: How I Learned to Stop Worrying and Love sns.lineplot. Such efficiency. Much wow.
sns.lineplot(data=df,
x=xdata,
y=ydata,
linewidth=0.6,
hue='field',
hue_norm=(-10, 10),
palette=sns.color_palette('coolwarm_r', as_cmap=True),
legend=None)
if x_axis == 'Energy (eV)':
ax.set_xlabel(x_axis)
if op == 'raw':
plt.title("Raw MCD " + title)
if op == 'avg':
plt.title("Averaged MCD " + title)
ax.plot([-10, 10], [0, 0], color='black', linestyle='-',
linewidth='1') #add 0T baseline
# ax.set_ylabel(r'$\Delta$A/A$_{\mathrm{max}}$ (x $10^{-3}$)')
ax.set_ylabel('MCD (mdeg)')
ax.set_xlim(2.7, 1.2)
ax.xaxis.set_major_locator(MultipleLocator(0.2))
ax.xaxis.set_minor_locator(AutoMinorLocator()) # auto set minor ticks
ax.set_ylim(-1.5, 1.5)
ax.yaxis.set_major_locator(MultipleLocator(0.5))
ax.yaxis.set_minor_locator(AutoMinorLocator()) # auto set minor ticks
ax2 = ax.twiny(
) # creates a new axis with invisible y and independent x on opposite side of first x-axis
ax2.set_xlabel(r'Wavelength (nm)')
ax2.set_xscale(
'function',
functions=(getWavelength,
getEnergy)) # set twin scale (convert degree eV to nm)
xmin, xmax = ax.get_xlim() # get left axis limits
ax2.set_xlim(
(getWavelength(xmax), getWavelength(xmin)
)) # apply function and set transformed values to right axis limits
ax2.xaxis.set_minor_locator(AutoMinorLocator()) # auto set minor ticks
ax2.plot(
[], []
) # set an invisible artist to twin axes to prevent falling back to initial values on rescale events
#Set tick parameters
ax.xaxis.set_tick_params(
which='major', size=5, width=0.8, direction='in'
) #axes.linewidth by default for matplotlib is 0.8, so that value is used here for aesthetic matching.
ax.xaxis.set_tick_params(which='minor', size=2, width=0.8, direction='in')
ax.yaxis.set_tick_params(which='major',
size=5,
width=0.8,
direction='in',
right='on')
ax.yaxis.set_tick_params(which='minor',
size=2,
width=0.8,
direction='in',
right='on')
ax2.xaxis.set_tick_params(which='major', size=5, width=0.8, direction='in')
ax2.xaxis.set_tick_params(which='minor', size=2, width=0.8, direction='in')
# plt.style.use('classic') #Doesn't seem to affect plot.
plt.tight_layout()
plt.style.use('seaborn-paper')
plt.savefig(op + '_mcd_' + title,
dpi=300,
transparent=False,
bbox_inches='tight')
plt.show()
#------------- plot variation coefficient ---------------#
width = 0.55
fig, axs = plt.subplots(1, 1, gridspec_kw={'hspace': 0, 'wspace': 0}, figsize=(12,5))
fig.suptitle("GPU training time variational coefficient (%)")
fig.subplots_adjust(bottom=0.2)
x = np.arange(len(tc_list))
axs.bar(x, vc_pct, width)
axs.set_xticks(x)
axs.set_xticklabels(tc_list, rotation=45, ha="right")
axs.set_xlabel('test cases')
axs.set_ylabel('variation coefficient (%)')
#axs.set_yticks(minor=True)
axs.get_yaxis().set_minor_locator(MultipleLocator(1))
#axs.legend()
axs.grid(which='both', axis='y', linestyle=':', color='black')
plt.savefig("./time_variation.png")
#------------- plot spread ---------------#
fig, axs = plt.subplots(1, 1, gridspec_kw={'hspace': 0, 'wspace': 0}, figsize=(12,5))
fig.suptitle("GPU training time spread")
fig.subplots_adjust(bottom=0.2)
x = np.arange(len(tc_list))
axs.bar(x, spread, width)
axs.set_xticks(x)
axs.set_xticklabels(tc_list, rotation=45, ha="right")
alpha=0.7)
ax.fill_between(x,
df_pc['SOLAR'].to_list(),
df_pc['HYDRO'].to_list(),
facecolor='orange',
alpha=0.7)
ax.fill_between(x,
df_pc['WIND'].to_list(),
df_pc['SOLAR'].to_list(),
facecolor='purple',
alpha=0.7)
ax.set_ylabel('Share of cumulative capacity', fontsize=9)
ax.set_xlabel('Year', fontsize=9)
ax.tick_params(labelsize=8)
ax.xaxis.set_major_locator(MultipleLocator(2))
ax.xaxis.set_minor_locator(MultipleLocator(1))
# ax.yaxis.set_major_locator(MultipleLocator(0.2))
# ax.yaxis.set_minor_locator(MultipleLocator(0.05))
# ax.set_ylim([0, 1])
fig.set_size_inches(4.5, 2.8)
legend_elements = [
Patch(facecolor='brown', label='Coal', alpha=0.7),
Patch(facecolor='green', label='Gas', alpha=0.7),
Patch(facecolor='blue', label='Hydro', alpha=0.7),
Patch(facecolor='orange', label='Solar', alpha=0.7),
Patch(facecolor='purple', label='Wind', alpha=0.7)
]
ax.legend(handles=legend_elements, fontsize=9, ncol=2, loc='lower left')
def plotHistogram(data, options):
colours = options.colour.split(',')
alphas = options.alpha.split(',')
columns = options.column.split(',')
leglabels = None
if options.leglabels:
leglabels = options.leglabels.split(',')
#fig = plt.figure(figsize=(6,3))
fig = plt.figure()
ax1 = fig.add_subplot(111)
#bins = n.linspace(round(float(options.binlower)), round(float(options.binupper)), int((float(options.binupper) - float(options.binlower))/float(options.binwidth))+1)
bins = n.linspace(
float(options.binlower), float(options.binupper),
int((float(options.binupper) - float(options.binlower)) /
float(options.binwidth)) + 1)
print(bins)
ml = MultipleLocator(float(options.majorticks))
ax1.xaxis.set_major_locator(ml)
ax1 = fig.add_subplot(111)
# May have more than one histogram to plot
i = 0
for d in data:
if len(colours) == 1:
colour = colours[0]
else:
colour = colours[i]
if len(alphas) == 1:
alpha = alphas[0]
else:
alpha = alphas[i]
print(n.sort(n.array(d)))
ax1.hist(n.sort(n.array(d)),
bins=bins,
color=colour,
edgecolor='black',
linewidth=0.5,
alpha=float(alpha))
i += 1
ax1.set_ylabel(options.ylabel)
for tl in ax1.get_yticklabels():
tl.set_color('k')
ax1.set_xlabel(options.xlabel)
#ax1.set_title('Classifier performance.')
if len(columns) > 1:
ax1.legend(columns, loc=1, frameon=False)
elif leglabels is not None:
ax1.legend(leglabels, loc=1, frameon=False)
ax1.text(0.8,
0.95,
options.plotlabel,
transform=ax1.transAxes,
va='top',
size=MEDIUM_SIZE)
ax1.text(0.1,
0.95,
options.panellabel,
transform=ax1.transAxes,
va='top',
size=MEDIUM_SIZE,
weight='bold')
ml = MultipleLocator(float(options.minorticks))
ax1.xaxis.set_minor_locator(ml)
ax1.get_xaxis().set_tick_params(which='both', direction='out')
ax1.set_xlim(float(options.binlower), float(options.binupper))
ax1.set_ylim(ymin=0)
if options.ylimit:
ax1.set_ylim(1, float(options.ylimit))
if options.log:
ax1.set_yscale('log')
if options.threshold is not None:
ax1.axvline(x=float(options.threshold), color='k', linestyle='--')
plt.tight_layout()
if options.outputFile is not None:
plt.savefig(options.outputFile)
else:
plt.show()
def plot_average_prices(results_dir, output_dir):
"""Plot average prices under different schemes"""
# Prices from different models
p_bau = analysis.get_average_prices(results_dir, 'bau_case.pickle', None,
'PRICES', -1)
p_rep = analysis.get_average_prices(results_dir, 'rep_case.pickle',
'stage_2_rep', 'PRICES', -1)
p_tax = analysis.get_average_prices(results_dir, 'rep_case.pickle',
'stage_1_carbon_tax', 'PRICES', -1)
p_price_dev_mppdc = analysis.get_average_prices(
results_dir, 'mppdc_price_change_deviation_case.pickle',
'stage_3_price_targeting', 'lamb', 1)
p_price_dev_heuristic = analysis.get_average_prices(
results_dir, 'heuristic_price_change_deviation_case.pickle',
'stage_3_price_targeting', 'PRICES', -1)
# Create figures
c = cl.to_numeric(
cl.flipper()['qual']['5']['Set1']
) # ['Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3'])
fig, ax = plt.subplots()
ax.plot(p_bau.index.tolist(),
p_bau['average_price_real'].tolist(),
color=scale_rgb(c[1]),
alpha=0.7,
linewidth=0.9)
ax.plot(p_tax.index.tolist(),
p_tax['average_price_real'].tolist(),
color=scale_rgb(c[0]),
alpha=0.7,
linewidth=0.9)
ax.plot(p_rep.index.tolist(),
p_rep['average_price_real'].tolist(),
color=scale_rgb(c[2]),
alpha=0.7,
linewidth=0.9)
ax.plot(p_price_dev_mppdc.index.tolist(),
p_price_dev_mppdc['average_price_real'].tolist(),
color=scale_rgb(c[3]),
alpha=0.7,
linewidth=0.9)
ax.plot(p_price_dev_heuristic.index.tolist(),
p_price_dev_heuristic['average_price_real'].tolist(),
color=scale_rgb(c[4]),
alpha=0.6,
linewidth=0.9)
fig.set_size_inches(3, 2.3)
ax.set_ylabel('Average price ($/MWh)', fontsize=9, labelpad=-0.1)
ax.set_xlabel('Year', fontsize=9)
ax.tick_params(labelsize=8)
ax.xaxis.set_major_locator(MultipleLocator(5))
ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(20))
ax.yaxis.set_minor_locator(MultipleLocator(5))
ax.legend(['BAU', 'Tax', 'REP', 'MPPDC', 'Heuristic'],
fontsize=7,
ncol=2,
frameon=False)
fig.subplots_adjust(left=0.16, bottom=0.18, top=0.98, right=0.98)
fig.savefig(os.path.join(output_dir, 'average_prices.png'))
fig.savefig(os.path.join(output_dir, 'average_prices.pdf'))
plt.show()
100.0 16310 -62.5 -73.5 21 0.02 285 36 406.7 406.8 406.7
'''
# Parse the data
sound_data = StringIO(data_txt)
p, h, T, Td = np.loadtxt(sound_data, usecols=range(0, 4), unpack=True)
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(6.5875, 6.2125))
ax = fig.add_subplot(111, projection='skewx')
plt.grid(True)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dicatated by the typical meteorological plot
ax.semilogy(T, p, 'r')
ax.semilogy(Td, p, 'g')
# An example of a slanted line at constant X
l = ax.axvline(0, color='b')
# Disables the log-formatting that comes with semilogy
ax.yaxis.set_major_formatter(ScalarFormatter())
ax.set_yticks(np.linspace(100, 1000, 10))
ax.set_ylim(1050, 100)
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.set_xlim(-50, 50)
plt.show()
ms=8,
mec="#7E2320",
mew=2) # set properties
axes.set_xlim(0, None) # limit for xaxis
axes.set_ylim(None, 0) # leave the ymax auto, but fix ymin
axes.set_xlabel(r"$k_{\rm B}T$ / $J$", labelpad=8)
axes.set_ylabel(r"$\kappa_{\rm xy}$ / $T$", labelpad=8)
##///Set ticks space and minor ticks space ///#
xtics = 0.3 # space between two ticks
mxtics = xtics / 2. # space between two minor ticks
majorFormatter = FormatStrFormatter(
'%g') # put the format of the number of ticks
axes.xaxis.set_major_locator(MultipleLocator(xtics))
axes.xaxis.set_major_formatter(majorFormatter)
axes.xaxis.set_minor_locator(MultipleLocator(mxtics))
axes.yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
axes.locator_params(axis='y', nbins=6)
plt.show()
folder = "../figures_sim/"
figure_name = "TS-kxy" + "_B_" + str(B) + "_J_" + str(J) + "_D_" + str(
D) + "_res_" + str(resolution_k) + ".pdf"
fig.savefig(folder + figure_name, bbox_inches="tight")
#//////////////////////////////////////////////////////////////////////////////#
def run(args, graph, labels, train_idx, val_idx, test_idx, evaluator, n_running):
# define model and optimizer
model = gen_model(args)
model = model.to(device)
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wd)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode="min", factor=0.5, patience=100, verbose=True, min_lr=1e-3
)
# training loop
total_time = 0
best_val_acc, best_test_acc, best_val_loss = 0, 0, float("inf")
accs, train_accs, val_accs, test_accs = [], [], [], []
losses, train_losses, val_losses, test_losses = [], [], [], []
for epoch in range(1, args.n_epochs + 1):
tic = time.time()
adjust_learning_rate(optimizer, args.lr, epoch)
loss, pred = train(model, graph, labels, train_idx, optimizer, args.use_labels)
acc = compute_acc(pred[train_idx], labels[train_idx], evaluator)
train_acc, val_acc, test_acc, train_loss, val_loss, test_loss = evaluate(
model, graph, labels, train_idx, val_idx, test_idx, args.use_labels, evaluator
)
lr_scheduler.step(loss)
toc = time.time()
total_time += toc - tic
# if val_acc > best_val_acc:
if val_loss < best_val_loss:
best_val_loss = val_loss
best_val_acc = val_acc
best_test_acc = test_acc
if epoch % args.log_every == 0:
print(f"Epoch: {epoch}/{args.n_epochs}")
print(
f"Loss: {loss.item():.4f}, Acc: {acc:.4f}\n"
f"Train/Val/Test loss: {train_loss:.4f}/{val_loss:.4f}/{test_loss:.4f}\n"
f"Train/Val/Test/Best val/Best test acc: {train_acc:.4f}/{val_acc:.4f}/{test_acc:.4f}/{best_val_acc:.4f}/{best_test_acc:.4f}"
)
for l, e in zip(
[accs, train_accs, val_accs, test_accs, losses, train_losses, val_losses, test_losses],
[acc, train_acc, val_acc, test_acc, loss, train_loss, val_loss, test_loss],
):
l.append(e)
print("*" * 50)
print(f"Average epoch time: {total_time / args.n_epochs}, Test acc: {best_test_acc}")
if args.plot_curves:
fig = plt.figure(figsize=(24, 24))
ax = fig.gca()
ax.set_xticks(np.arange(0, args.n_epochs, 100))
ax.set_yticks(np.linspace(0, 1.0, 101))
ax.tick_params(labeltop=True, labelright=True)
for y, label in zip([accs, train_accs, val_accs, test_accs], ["acc", "train acc", "val acc", "test acc"]):
plt.plot(range(args.n_epochs), y, label=label)
ax.xaxis.set_major_locator(MultipleLocator(100))
ax.xaxis.set_minor_locator(AutoMinorLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(0.01))
ax.yaxis.set_minor_locator(AutoMinorLocator(2))
plt.grid(which="major", color="red", linestyle="dotted")
plt.grid(which="minor", color="orange", linestyle="dotted")
plt.legend()
plt.tight_layout()
plt.savefig(f"gcn_acc_{n_running}.png")
fig = plt.figure(figsize=(24, 24))
ax = fig.gca()
ax.set_xticks(np.arange(0, args.n_epochs, 100))
ax.tick_params(labeltop=True, labelright=True)
for y, label in zip(
[losses, train_losses, val_losses, test_losses], ["loss", "train loss", "val loss", "test loss"]
):
plt.plot(range(args.n_epochs), y, label=label)
ax.xaxis.set_major_locator(MultipleLocator(100))
ax.xaxis.set_minor_locator(AutoMinorLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(0.1))
ax.yaxis.set_minor_locator(AutoMinorLocator(5))
plt.grid(which="major", color="red", linestyle="dotted")
plt.grid(which="minor", color="orange", linestyle="dotted")
plt.legend()
plt.tight_layout()
plt.savefig(f"gcn_loss_{n_running}.png")
return best_val_acc, best_test_acc
ax.set_ylabel("Counts")
ax.set_xticklabels([])
ax.set_xticks([])
ax.legend(["2b SR NN Regression", "2b SR Data"])
ratio = [m / d if d > 0 else 100 for m, d in zip(modelmhh, datamhh)]
err = [r / np.sqrt(d) if d > 0 else 0 for r, d in zip(ratio, datamhh)]
ax = plt.subplot(gs[1])
print(ratio)
ax.errorbar(XData, ratio, yerr=err, fmt='k.')
ax.plot([mhhbins[0], mhhbins[-1]], [1, 1], 'k--', linewidth=1)
ax.set_ylim(0.75, 1.25)
ax.set_ylim(0.9, 1.1)
ax.set_xlabel("$m_{hh}$" + " (GeV)")
ax.set_ylabel("Pred./Data")
ax.yaxis.set_major_locator(MultipleLocator(0.05))
ax.yaxis.set_minor_locator(MultipleLocator(0.01))
ax.xaxis.set_major_locator(MultipleLocator(100))
ax.xaxis.set_minor_locator(MultipleLocator(25))
plt.savefig(ModelName + "mhhSR.png")
plt.show()
else:
modeldfSR = modeldf.loc[binInSR(modeldf["m_h1"], modeldf["m_h2"])]
modelmhh = list(integrate_fmp(modeldfSR)["pdf"])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.step(mhhbins, modelmhh + [modelmhh[-1]], 'r', linewidth=2, where='post')
ax.set_xlabel("$m_{hh}$" + " (GeV)")
ax.set_ylabel("Counts")
ax.legend(["4b SR NN Regression"])
ax.xaxis.set_major_locator(MultipleLocator(100))
label='Initial UHG',
ls='--',
linewidth='3',
zorder=5,
alpha=0.75)
#ax1.fill_between(x,UHG_flow,facecolor='gray', alpha=0.25)
#ax1.minorticks_on()
ax1.grid(which='major',
axis='both',
color='black',
linestyle='-',
zorder=3)
ax1.grid(which='minor', axis='both', color='grey', linestyle='-', zorder=3)
majorLocator = MultipleLocator(interval)
ax1.xaxis.set_major_locator(majorLocator)
ax1.yaxis.set_minor_locator(AutoMinorLocator(2))
ax1.set_xlabel('Time (hr)')
ax1.set_ylabel('Flow (cfs)')
#Make tick labels smaller/rotate for long UHGs
if num_ordinates >= 15:
for label in ax1.xaxis.get_ticklabels():
label.set_fontsize(8)
plt.xticks(rotation=90)
if num_ordinates >= 24:
for label in ax1.xaxis.get_ticklabels():
label.set_fontsize(6)
plt.figure()
ax = plt.gca()
for fname in fnames:
print(fname)
curve = np.load(fname)
print((curve[-1, 0] - curve[0, 0]) / 3600)
last = curve[0]
c_v = [[0, curve[0, 2], curve[0, 3]]]
for x in curve[1:]:
#print(x)
time_diff = x[0] - last[0]
Ah = time_diff / 3600 * -x[1]
c_v.append([c_v[-1][0] + Ah, x[2], x[3]])
last = x
print(np.array(c_v[-1]))
c_v = np.array(c_v)
plt.plot(c_v[:, 0] * 1E3,
c_v[:, 2],
label=fname.split("_")[-1].split(".npy")[0])
#plt.plot(curve[:,3].astype('float'))
lgd = ax.legend()
plt.grid(True, 'both', 'both')
ml = MultipleLocator(1)
ax.xaxis.set_minor_locator(ml)
ax.axhline(y=2.35, lw=1, color='r')
ax.axhline(y=2.33, lw=1, color='r')
plt.ylabel('Cell Voltage')
plt.xlabel('Capacity Discharged (mAh)')
plt.savefig('plot.pdf', format='pdf')
#!/usr/bin/python
import sys
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import pandas
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
plt.clf()
fig=plt.figure(figsize=(40, 40))
ax = fig.add_subplot(111)
mat=pandas.read_csv(sys.argv[1],header=0,index_col=0)
# min=0 and max=1 is set to just distinguish absent and present
# remove those parameters to see colors scaled by value
ax.matshow(mat,cmap=cm.gray, interpolation='nearest', vmin=0, vmax=1)
locator=MultipleLocator(1)
locator.MAXTICKS = 10000
ax.xaxis.set_major_locator(locator)
ax.set_xticklabels([""]+mat.columns.tolist(), rotation=90)
ax.yaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_formatter(FormatStrFormatter('%s'))
ax.set_yticklabels([""]+mat.index.tolist())
ax.tick_params(axis='both', which='both', labelsize=8, direction='out',
labelleft='on', labelright='off', labelbottom='off',
labeltop='on', left='on', right='off', bottom='off',
top='on')
plt.savefig(sys.argv[2], bbox_inches='tight', dpi = 100)
def makeKlusterImage(klusterid, kl, outputpath):
""" Create the kluster image. """
# FIXME: Make configurable.
pixels = kl.getPixelMap()
x_min = kl.getXMin()
x_max = kl.getXMax()
y_min = kl.getYMin()
y_max = kl.getYMax()
w = kl.getWidth()
h = kl.getHeight()
## The maximum count value.
C_max = kl.getMaxCountValue()
x_bar = kl.getXUW()
y_bar = kl.getYUW()
radius = kl.getRadiusUW()
m, c, sumR = kl.getLineOfBestFitValues()
# Create the figure.
plt.close('all')
figsize = 5.0
## The figure for the cluster image.
blobfig = plt.figure(1,
figsize=(figsize * 1.27, figsize),
dpi=150,
facecolor='w',
edgecolor='w')
# Set the beyond-frame background colour.
blobfigax = blobfig.add_subplot(111, axisbg='#222222')
# Add the frame background (blue).
blobfigax.add_patch(plt.Rectangle((0, 0), 256, 256, facecolor='#82bcff'))
# Add a grid.
plt.grid(1)
# Select the "hot" colour map for the pixel counts.
cmap = plt.cm.hot
colax, _ = colorbar.make_axes(plt.gca())
col_max = 10 * (np.floor(C_max / 10.) + 1)
colorbar.ColorbarBase(colax,
cmap=cmap,
norm=colors.Normalize(vmin=0, vmax=col_max))
# Add the line of best fit.
addLineOfBestFit(blobfigax, m, c)
# Add the radius circle.
addRadiusCircle(blobfigax, x_bar, y_bar, radius)
# Loop over the pixels and plot them.
for X, C in pixels.iteritems():
x = X % 256
y = X / 256
scaled_C = float(C) / float(col_max)
blobfigax.add_patch(
plt.Rectangle((x, y), 1, 1, facecolor=cmap(scaled_C)))
# Set the axis limits based on the cluster radius.
b = 3 # border
xlim_min = x_bar - (np.floor(radius) + b)
xlim_max = x_bar + (np.floor(radius) + b)
ylim_min = y_bar - (np.floor(radius) + b)
ylim_max = y_bar + (np.floor(radius) + b)
blobfigax.set_xlim([xlim_min, xlim_max])
blobfigax.set_ylim([ylim_min, ylim_max])
# Set the axis tick mark spacing.
blobfigax.xaxis.set_major_locator(MultipleLocator(10))
blobfigax.yaxis.set_major_locator(MultipleLocator(10))
# Save the figure.
blobfig.savefig(outputpath + "/%s.png" % (klusterid))
