def plot5_1X(minor):
params = {'figure.figsize': [6.15, 5.0]}
rcParams.update(params)
reinforce_epsilon = mmread("./out/ex5_%d_reinforce_epsilon.mtx" % minor)
enac_epsilon = mmread("./out/ex5_%d_enac_epsilon.mtx" % minor)
reinforce_action_mean = \
mmread("./out/ex5_%d_reinforce_action_mean.mtx" % minor)
reinforce_action_std = \
mmread("./out/ex5_%d_reinforce_action_std.mtx" % minor)
enac_action_mean = mmread("./out/ex5_%d_enac_action_mean.mtx" % minor)
enac_action_std = mmread("./out/ex5_%d_enac_action_std.mtx" % minor)
actions = [(reinforce_action_mean, reinforce_action_std, reinforce_epsilon,
"REINFORCE", "Sigma", None, None),
(enac_action_mean, enac_action_std, enac_epsilon, "ENAC",
"Sigma", None, None)]
for ai in [0, 1]:
figure(ai)
plot_results(actions, ai, "Action (\%)", "Market Period")
if tex:
savefig('./out/fig5_%d_action_a%d.pdf' % (minor, ai + 1))
# savefig('./out/fig5_%d_action_a%d.eps' % (minor, ai + 1))
else:
savefig('./out/fig5_%d_action_a%d.png' % (minor, ai + 1))
def __init__(self, tm, showOverlaps=False, showOverlapsValues=False):
self.tm = tm
self.showOverlaps = showOverlaps
self.showOverlapsValues = showOverlapsValues
self.encodings = []
self.resets = []
self.numSegmentsPerCell = []
self.numSynapsesPerSegment = []
import matplotlib.pyplot as plt
self.plt = plt
import matplotlib.cm as cm
self.cm = cm
from pylab import rcParams
if self.showOverlaps and self.showOverlapsValues:
rcParams.update({'figure.figsize': (20, 20)})
else:
rcParams.update({'figure.figsize': (6, 12)})
rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
rcParams.update({'ytick.labelsize': 8})
def __init__(self, tm, showOverlaps=False, showOverlapsValues=False):
self.tm = tm
self.showOverlaps = showOverlaps
self.showOverlapsValues = showOverlapsValues
self.encodings = []
self.resets = []
self.numSegmentsPerCell = []
self.numSynapsesPerSegment = []
import matplotlib.pyplot as plt
self.plt = plt
import matplotlib.cm as cm
self.cm = cm
from pylab import rcParams
if self.showOverlaps and self.showOverlapsValues:
rcParams.update({'figure.figsize': (20, 20)})
else:
rcParams.update({'figure.figsize': (6, 12)})
rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
rcParams.update({'ytick.labelsize': 8})
def getErrorbarFigures(title, X, Ys, stdDevs, plotTitles, xAxisLabel,
yAxisLabels, gridFormat):
"""
Plots the specified data and saves specified plot to file
"""
rcParams['figure.figsize'] = 15, 15
fig = plt.figure()
fig.suptitle(title)
fig.subplots_adjust(left=None,
right=None,
bottom=None,
top=None,
wspace=None,
hspace=0.35)
plt.ion()
plt.show()
rcParams.update({'font.size': 12})
for i, y in enumerate(Ys):
ax = fig.add_subplot(gridFormat + 1 + i)
ax.set_title(plotTitles[i])
ax.set_xlabel(xAxisLabel)
ax.set_ylabel(yAxisLabels[i])
ax.axis([0, max(X) + 10, 0, 20])
ax.errorbar(X, y, stdDevs[i])
return fig
def SetForEps(proport=0.75, fig_width_pt=455.24, xylabel_fs=9, leg_fs=8, text_fs=9, xtick_fs=7, ytick_fs=7):
"""
Set figure proportions
======================
"""
# fig_width_pt = 455.24411 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*proport # height in inches
fig_size = [fig_width,fig_height]
#params = {'mathtext.fontset':'stix', # 'cm', 'stix', 'stixsans', 'custom'
params = {
'backend' : 'ps',
'axes.labelsize' : xylabel_fs,
'font.size' : text_fs,
'legend.fontsize' : leg_fs,
'xtick.labelsize' : xtick_fs,
'ytick.labelsize' : ytick_fs,
'text.usetex' : True, # very IMPORTANT to avoid Type 3 fonts
'ps.useafm' : True, # very IMPORTANT to avoid Type 3 fonts
'pdf.use14corefonts' : True, # very IMPORTANT to avoid Type 3 fonts
'figure.figsize' : fig_size,
}
MPLclose()
rcdefaults()
rcParams.update(params)
def plot5_1X(minor):
params = {'figure.figsize': [6.15, 5.0]}
rcParams.update(params)
reinforce_epsilon = mmread("./out/ex5_%d_reinforce_epsilon.mtx" % minor)
enac_epsilon = mmread("./out/ex5_%d_enac_epsilon.mtx" % minor)
reinforce_action_mean = \
mmread("./out/ex5_%d_reinforce_action_mean.mtx" % minor)
reinforce_action_std = \
mmread("./out/ex5_%d_reinforce_action_std.mtx" % minor)
enac_action_mean = mmread("./out/ex5_%d_enac_action_mean.mtx" % minor)
enac_action_std = mmread("./out/ex5_%d_enac_action_std.mtx" % minor)
actions = [
(reinforce_action_mean, reinforce_action_std, reinforce_epsilon,
"REINFORCE", "Sigma", None, None),
(enac_action_mean, enac_action_std, enac_epsilon,
"ENAC", "Sigma", None, None)
]
for ai in [0, 1]:
figure(ai)
plot_results(actions, ai, "Action (\%)", "Market Period")
if tex:
savefig('./out/fig5_%d_action_a%d.pdf' % (minor, ai + 1))
# savefig('./out/fig5_%d_action_a%d.eps' % (minor, ai + 1))
else:
savefig('./out/fig5_%d_action_a%d.png' % (minor, ai + 1))
def do_plot(date, flux, status=0):
xmin = min(date)
xmax = max(date)
ymin = min(flux)
ymax = max(flux)
xr = xmax - xmin
yr = ymax - ymin
try:
params = {
"backend": "png",
"axes.linewidth": 2.5,
"axes.labelsize": 24,
"axes.font": "sans-serif",
"axes.fontweight": "bold",
"text.fontsize": 12,
"legend.fontsize": 12,
"xtick.labelsize": 16,
"ytick.labelsize": 16,
}
rcParams.update(params)
except:
print("ERROR -- KEPCLIP: install latex for scientific plotting")
status = 1
if status == 0:
# plt.figure(figsize=[12,5])
plt.clf()
# plt.axes([0.2,0.2,0.94,0.88])
# ltime = [date[0]]; ldata = [flux[0]]
# for i in range(1,len(flux)):
# if (date[i-1] > date[i] - 0.025):
# ltime.append(date[i])
# ldata.append(flux[i])
# else:
# ltime = n.array(ltime, dtype='float64')
# ldata = n.array(ldata, dtype='float64')
# plt.plot(ltime,ldata,color='#0000ff',linestyle='-'
# ,linewidth=1.0)
# ltime = []; ldata = []
# ltime = n.array(ltime, dtype='float64')
# ldata = n.array(ldata, dtype='float64')
plt.plot(date, flux, color="#0000ff", linestyle="-", linewidth=1.0)
date = n.insert(date, [0], [date[0]])
date = n.append(date, [date[-1]])
flux = n.insert(flux, [0], [0.0])
flux = n.append(flux, [0.0])
plt.fill(date, flux, fc="#ffff00", linewidth=0.0, alpha=0.2)
plt.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
if ymin - yr * 0.01 <= 0.0:
plt.ylim(1.0e-10, ymax + yr * 0.01)
else:
plt.ylim(ymin - yr * 0.01, ymax + yr * 0.01)
xlab = "BJD"
ylab = "e- / cadence"
plt.xlabel(xlab, {"color": "k"})
plt.ylabel(ylab, {"color": "k"})
plt.ion()
plt.grid()
plt.ioff()
def days_of_week_spending(file_data):
rcParams['figure.figsize'] = [16, 9]
rcParams.update({'figure.autolayout': True})
sns.set(style="dark")
df = pd.read_csv(file_data)
df["category"] = df["category"].map(all_categories)
df["cost"] = df["cost"].astype("float64")
df["cost"] = df["cost"].apply(lambda x: x / 100)
m = file_data.index(".")
result = df.groupby(["dayOfWeek", "category"])["cost"] \
.agg([np.sum]) \
.reindex(['Пн', 'Вт', 'Ср', 'Чт', 'Пт', 'Сб', "Вс"], level='dayOfWeek')
g = result["sum"].groupby(level=0, group_keys=False) \
.nlargest(4)
gr = sns.factorplot(x="dayOfWeek",
y="sum",
hue="category",
data=g.reset_index(),
kind="bar",
palette="Paired",
size=6,
aspect=1.5,
legend=False)
plt.legend(loc='upper right')
gr.savefig(file_data[:m] + "_plot.png")
plt.clf()
def SetForPng(proport=0.75,
fig_width_pt=455.24,
dpi=150,
xylabel_fs=9,
leg_fs=8,
text_fs=9,
xtick_fs=7,
ytick_fs=7):
"""
Set figure proportions
======================
"""
inches_per_pt = 1.0 / 72.27 # Convert pt to inch
fig_width = fig_width_pt * inches_per_pt # width in inches
fig_height = fig_width * proport # height in inches
fig_size = [fig_width, fig_height]
params = {
'axes.labelsize': xylabel_fs,
'font.size': text_fs,
'legend.fontsize': leg_fs,
'xtick.labelsize': xtick_fs,
'ytick.labelsize': ytick_fs,
'figure.figsize': fig_size,
'savefig.dpi': dpi,
}
#utl.Ff(&bb, "SetFontSize(%s)\n", args)
#utl.Ff(&bb, "rcParams.update({'savefig.dpi':%d})\n", dpi)
MPLclose()
rcdefaults()
rcParams.update(params)
def SetForEps(proport=0.75,
fig_width_pt=455.24,
xylabel_fs=9,
leg_fs=8,
text_fs=9,
xtick_fs=7,
ytick_fs=7,
text_usetex=True,
mplclose=True):
"""
Set figure proportions
======================
"""
# fig_width_pt = 455.24411 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0 / 72.27 # Convert pt to inch
fig_width = fig_width_pt * inches_per_pt # width in inches
fig_height = fig_width * proport # height in inches
fig_size = [fig_width, fig_height]
#params = {'mathtext.fontset':'stix', # 'cm', 'stix', 'stixsans', 'custom'
params = {
'backend': 'ps',
'axes.labelsize': xylabel_fs,
'font.size': text_fs,
'legend.fontsize': leg_fs,
'xtick.labelsize': xtick_fs,
'ytick.labelsize': ytick_fs,
'text.usetex': text_usetex, # very IMPORTANT to avoid Type 3 fonts
'ps.useafm': True, # very IMPORTANT to avoid Type 3 fonts
'pdf.use14corefonts': True, # very IMPORTANT to avoid Type 3 fonts
'figure.figsize': fig_size,
}
if mplclose: MPLclose()
rcdefaults()
rcParams.update(params)
def do_plot(date,flux,status=0):
xmin = min(date)
xmax = max(date)
ymin = min(flux)
ymax = max(flux)
xr = xmax - xmin
yr = ymax - ymin
try:
params = {'backend': 'png','axes.linewidth': 2.5,
'axes.labelsize': 24,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 16,
'ytick.labelsize': 16}
rcParams.update(params)
except:
print 'ERROR -- KEPCLIP: install latex for scientific plotting'
status = 1
if status == 0:
# plt.figure(figsize=[12,5])
plt.clf()
# plt.axes([0.2,0.2,0.94,0.88])
# ltime = [date[0]]; ldata = [flux[0]]
# for i in range(1,len(flux)):
# if (date[i-1] > date[i] - 0.025):
# ltime.append(date[i])
# ldata.append(flux[i])
# else:
# ltime = n.array(ltime, dtype='float64')
# ldata = n.array(ldata, dtype='float64')
# plt.plot(ltime,ldata,color='#0000ff',linestyle='-'
# ,linewidth=1.0)
# ltime = []; ldata = []
# ltime = n.array(ltime, dtype='float64')
# ldata = n.array(ldata, dtype='float64')
plt.plot(date,flux,color='#0000ff',linestyle='-',linewidth=1.0)
date = n.insert(date,[0],[date[0]])
date = n.append(date,[date[-1]])
flux = n.insert(flux,[0],[0.0])
flux = n.append(flux,[0.0])
plt.fill(date,flux,fc='#ffff00',linewidth=0.0,alpha=0.2)
plt.xlim(xmin-xr*0.01,xmax+xr*0.01)
if ymin-yr*0.01 <= 0.0:
plt.ylim(1.0e-10,ymax+yr*0.01)
else:
plt.ylim(ymin-yr*0.01,ymax+yr*0.01)
xlab = 'BJD'
ylab = 'e- / cadence'
plt.xlabel(xlab, {'color' : 'k'})
plt.ylabel(ylab, {'color' : 'k'})
plt.ion()
plt.grid()
plt.ioff()
def pylab_setup():
from pylab import rcParams
params = {'backend': 'qt',
'axes.labelsize': 8,
'text.fontsize': 8,
'legend.fontsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8}
rcParams.update(params)
def matrc4X6():
MatPlotParams = {'axes.titlesize': 12, 'axes.linewidth' : 1.0, 'axes.labelsize': 12, 'xtick.labelsize': 10, 'ytick.labelsize': 10, 'xtick.major.size': 12, 'ytick.major.size' : 12, 'xtick.minor.size': 10, 'ytick.minor.size': 10, 'figure.figsize' : [12.0, 10.0], 'xtick.major.pad' : 6, 'ytick.major.pad' : 4, 'figure.subplot.hspace' : 0.0}
rcParams.update(MatPlotParams)
pylab.subplots_adjust(left = 0.07, # the left side of the subplots of the figure
right = 0.97, # the right side of the subplots of the figure
bottom = 0.05, # the bottom of the subplots of the figure
top = 0.95, # the top of the subplots of the figure
wspace = 0.15, # the amount of width reserved for blank space between subplots
hspace = 0.15) # the amount of height reserved for white space between subplots
def galplot6():
MatPlotParams = {'axes.titlesize': 12, 'axes.linewidth' : 1.0, 'axes.labelsize': 12, 'xtick.labelsize': 10, 'ytick.labelsize': 10, 'xtick.major.size': 12, 'ytick.major.size' : 12, 'xtick.minor.size': 10, 'ytick.minor.size': 10, 'figure.figsize' : [10.0, 10.5], 'xtick.major.pad' : 6, 'ytick.major.pad' : 6, 'figure.subplot.hspace' : 0.0,'legend.fontsize': 10}
rcParams.update(MatPlotParams)
pylab.subplots_adjust(left = 0.2, # the left side of the subplots of the figure
right = 0.8, # the right side of the subplots of the figure
bottom = 0.1,#.05 # the bottom of the subplots of the figure
top = 0.95, # the top of the subplots of the figure
wspace = 0.30, # the amount of width reserved for blank space between subplots
hspace = 0.45)#.15 # the amount of height reserved for white space between subplots
def imsave(filename, data, **kwargs):
figsize = (pylab.array(data.shape) / 100.0)[::-1]
rcParams.update({'figure.figsize': figsize})
fig = pylab.figure(figsize=figsize)
pylab.axes([0, 0, 1, 1])
pylab.axis('off')
fig.set_size_inches(figsize)
pylab.imshow(data, origin='lower', **kwargs)
pylab.savefig(filename, facecolor='black', edgecolor='black', dpi=100)
pylab.close(fig)
def imsave( filename, data, **kwargs ):
figsize = (pylab.array(data.shape)/100.0)[::-1]
rcParams.update( {'figure.figsize':figsize} )
fig = pylab.figure( figsize=figsize )
pylab.axes( [0,0,1,1] )
pylab.axis( 'off' )
fig.set_size_inches( figsize )
pylab.imshow( data, origin='lower', **kwargs )
pylab.savefig( filename, facecolor='black', edgecolor='black', dpi=100 )
pylab.close( fig )
def graphEpocs(self):
"""Graph the average k-fold training and test error for all tested epocs"""
rcParams.update(self._PRAMS)
plot(self.epocsRange, self.epocsErrors[0], 'r-', label="Training Error", linewidth=2)
plot(self.epocsRange, self.epocsErrors[1], 'b-', label="Test Error", linewidth=2)
xlabel('Number Epocs')
ylabel('Error')
title('Epocs vs Error')
legend()
show()
def mhd_shocktube(P, x=(0,1), **kwargs):
from pylab import sqrt, linspace, subplot, plot, text, xlabel, figure, show
from pylab import subplots_adjust, setp, gca, LinearLocator, rcParams, legend
rho, pre = P[:,0], P[:,1]
vx, vy, vz = P[:,2], P[:,3], P[:,4]
Bx, By, Bz = P[:,5], P[:,6], P[:,7]
plot_args = { }
plot_args['marker'] = kwargs.get('marker', 'o')
plot_args['c' ] = kwargs.get('c' , 'k')
plot_args['mfc' ] = kwargs.get('mfc' , 'None')
plot_args.update(kwargs)
rcParams.update({'axes.labelsize':16, 'ytick.major.pad':8})
X = linspace(x[0],x[1],P.shape[0])
g = 1 / sqrt(1-(vx**2+vy**2+vz**2))
ax = subplot(2,3,1)
plot(X,rho, **plot_args)
text(0.9,0.85, r"$\rho$", transform = ax.transAxes, fontsize=20)
setp(ax.get_xticklabels(), visible=False)
if 'label' in plot_args: legend(loc='upper left')
ax = subplot(2,3,2)
plot(X,pre, **plot_args)
text(0.9,0.85, r"$P$", transform = ax.transAxes, fontsize=20)
setp(ax.get_xticklabels(), visible=False)
ax = subplot(2,3,3)
plot(X, g, **plot_args)
text(0.9,0.85, r"$\gamma$", transform = ax.transAxes, fontsize=20)
setp(ax.get_xticklabels(), visible=False)
ax = subplot(2,3,4)
plot(X, vx, **plot_args)
text(0.9,0.85, r"$v_x$", transform = ax.transAxes, fontsize=20)
xlabel(r"$x$")
ax = subplot(2,3,5)
plot(X, vy, **plot_args)
text(0.9,0.85, r"$v_y$", transform = ax.transAxes, fontsize=20)
xlabel(r"$x$")
ax = subplot(2,3,6)
plot(X, By, **plot_args)
text(0.9,0.85, r"$B_y$", transform = ax.transAxes, fontsize=20)
xlabel(r"$x$")
subplots_adjust(hspace=0.15)
def SetFontSize(xylabel_fs=9, leg_fs=8, text_fs=9, xtick_fs=7, ytick_fs=7):
"""
Set font sizes
==============
"""
params = {
'axes.labelsize' : xylabel_fs,
'legend.fontsize' : leg_fs,
'font.size' : text_fs,
'xtick.labelsize' : xtick_fs,
'ytick.labelsize' : ytick_fs,
}
rcParams.update(params)
def plot_weather(self, start, end):
df = self.read_weather(start, end).dropna().reset_index()
df['weather'] = df.apply(self.weather_category, axis=1)
from pylab import rcParams
rcParams['figure.figsize'] = 40, 10
rcParams.update({'font.size': 22})
weather_df = df.groupby(['month', 'weather'
]).aqi.count().unstack().reset_index()
weather_df.plot.bar(
x='month',
y=[rain_index, overcast_index, cloudy_index, sunny_index])
plt.show()
def SetFontSize(xylabel_fs=9, leg_fs=8, text_fs=9, xtick_fs=7, ytick_fs=7):
"""
Set font sizes
==============
"""
params = {
'axes.labelsize': xylabel_fs,
'legend.fontsize': leg_fs,
'font.size': text_fs,
'xtick.labelsize': xtick_fs,
'ytick.labelsize': ytick_fs,
}
rcParams.update(params)
def SetFontSize(xylabel_fs=9, leg_fs=8, text_fs=9, xtick_fs=7, ytick_fs=7):
"""
Set font sizes
==============
"""
params = {
"axes.labelsize": xylabel_fs,
"legend.fontsize": leg_fs,
"font.size": text_fs,
"xtick.labelsize": xtick_fs,
"ytick.labelsize": ytick_fs,
}
rcParams.update(params)
def categories_spending(file_data):
rcParams['figure.figsize'] = [16, 9]
rcParams.update({'figure.autolayout': True})
df = pd.read_csv(file_data)
df["category"] = df["category"].map(all_categories)
df["cost"] = df["cost"].astype("float64")
df["cost"] = df["cost"].apply(lambda x: x / 100)
m = file_data.index(".")
ax = df.groupby(["category", "date"])["cost"] \
.sum().unstack("category").plot()
plt.legend(loc='upper right')
plt.savefig(file_data[:m] + "_plot.png")
plt.clf()
def pub_plots(xmaj = 5, xmin = 1, xstr = '%03.2f', ymaj = 5, ymin = 1, ystr = '%d'):
MatPlotParams = {'axes.yaxis.labelpad': 10,'axes.xaxis.labelpad': 10, 'axes.titlesize': 10, 'axes.linewidth' : 1.5, 'axes.labelsize': 10, 'xtick.labelsize': 10, 'ytick.labelsize': 10, 'xtick.major.size': 8, 'ytick.major.size' : 8, 'xtick.minor.size': 4, 'ytick.minor.size': 4, 'xtick.major.pad' : 6, 'ytick.major.pad' : 6}
rcParams.update(MatPlotParams)
xmajLocator = MultipleLocator(xmaj)
xmajFormatter = FormatStrFormatter(xstr)
xminLocator = MultipleLocator(xmin)
ymajLocator = MultipleLocator(ymaj)
ymajFormatter = FormatStrFormatter(ystr)
yminLocator = MultipleLocator(ymin)
data_holder = plot_dats(xmajLocator, xmajFormatter, xminLocator, ymajLocator,
ymajFormatter, yminLocator)
return data_holder
def SetForEps (proport=0.75, fig_width_pt=455.24):
# fig_width_pt = 455.24411 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*proport # height in inches
fig_size = [fig_width,fig_height]
params = {'mathtext.fontset':'stix', # 'cm', 'stix', 'stixsans', 'custom'
'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 8,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': False,
'figure.figsize': fig_size}
rcParams.update(params)
def SetForEps (proport=0.75, fig_width_pt=455.24):
# fig_width_pt = 455.24411 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*proport # height in inches
fig_size = [fig_width,fig_height]
params = {'mathtext.fontset':'stix', # 'cm', 'stix', 'stixsans', 'custom'
'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 8,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': False,
'figure.figsize': fig_size}
rcParams.update(params)
def DrawCrossover(A, B, a, b, pos):
"""
DrawCrossover draws crossover process
"""
rcParams.update({'figure.figsize':[800/72.27,400/72.27]})
DrawChromo('A', A, pos, 0.35, 0)
DrawChromo('B', B, pos, 0.25, 1)
DrawChromo('a', a, pos, 0.10, 0, blue='#e3a9a9')
DrawChromo('b', b, pos, 0.00, 0, red='#c8d0e3')
axis('equal')
axis([0, 1.2, 0, 0.4])
gca().get_yaxis().set_visible(False)
gca().get_xaxis().set_visible(False)
for dir in ['left', 'right', 'top', 'bottom']:
gca().spines[dir].set_visible(False)
gca().add_patch(FancyArrowPatch([0.6,0.25], [0.6, 0.2], fc='#9fffde', mutation_scale=30))
def plotExperimentState(runner, plotVerbosity, numWorlds, numElems, isOnline, experimentPhase):
if plotVerbosity >= 1:
rcParams["figure.figsize"] = PLOT_WIDTH, PLOT_HEIGHT
rcParams.update({"font.size": 14})
title = "worlds: {0}, elements: {1}, online: {2}, phase: {3}".format(
numWorlds, numElems, isOnline, experimentPhase
)
# runner.tp.mmGetPlotConnectionsPerColumn(title=title)
if plotVerbosity >= 2:
# runner.tm.mmGetCellActivityPlot(title=title, activityType="activeCells",
# showReset=True,
# resetShading=PLOT_RESET_SHADING)
# runner.tm.mmGetCellActivityPlot(title=title,
# activityType="predictedActiveCells",
# showReset=True,
# resetShading=PLOT_RESET_SHADING)
runner.tp.mmGetCellActivityPlot(title=title, showReset=True, resetShading=PLOT_RESET_SHADING)
writeToCsv(runner)
def plotExperimentState(runner, plotVerbosity, numWorlds, numElems, isOnline,
experimentPhase):
if plotVerbosity >= 1:
rcParams['figure.figsize'] = PLOT_WIDTH, PLOT_HEIGHT
rcParams.update({'font.size': 14})
title = "worlds: {0}, elements: {1}, online: {2}, phase: {3}".format(
numWorlds, numElems, isOnline, experimentPhase)
# runner.tp.mmGetPlotConnectionsPerColumn(title=title)
if plotVerbosity >= 2:
# runner.tm.mmGetCellActivityPlot(title=title, activityType="activeCells",
# showReset=True,
# resetShading=PLOT_RESET_SHADING)
# runner.tm.mmGetCellActivityPlot(title=title,
# activityType="predictedActiveCells",
# showReset=True,
# resetShading=PLOT_RESET_SHADING)
runner.tp.mmGetCellActivityPlot(title=title, showReset=True,
resetShading=PLOT_RESET_SHADING)
writeToCsv(runner)
def aniStep(step):
mesh.inject(int(1.1e6/mesh.dt/frames))
with plt.style.context('fivethirtyeight'):
rcParams.update({'figure.autolayout': True})
gs = gridspec.GridSpec(2, 2)
AC = plt.subplot(gs[:,0])
#BW = plt.subplot(gs[:,3:])
AC.scatter(ArrowCanyon.d13c,ArrowCanyon.SAMP_HEIGHT,s=10,color=[.7,.7,.7],edgecolor='none')
AC.set_xlabel('$\delta$13C')
AC.set_ylabel('height(m)')
AC.set_ylim([0,120])
AC.set_xlim([-10,6])
BattleshipWash.d44ca=BattleshipWash.d44ca[BattleshipWash.d44ca<-.9]
cmin=round(np.min([ArrowCanyon.d44ca.min(),BattleshipWash.d44ca.min()]),1)
cmax=round(np.max([ArrowCanyon.d44ca.max(),BattleshipWash.d44ca.max()]),1)
specCM=plt.get_cmap('Spectral')
minCa=-1.4
rangeCa=.4
for i in range(meshX-1):
avCa=np.mean(mesh.printBox('rock','d44ca')[1,i:i+2])
AC.plot(mesh.printBox('rock','d13c')[1,i:i+2],np.linspace(meshX-(i-1),meshX-i,2),color=specCM(np.abs(minCa-avCa)/rangeCa))
AC.scatter(BattleshipWash.d13c,BattleshipWash.SAMP_HEIGHT-240.0,s=10,color=[.7,.7,.7],edgecolor='none')
cmax=-1.0
d44ca=AC.scatter(ArrowCanyon.d13c,ArrowCanyon.SAMP_HEIGHT,c=ArrowCanyon.d44ca,cmap='Spectral',vmin=cmin,vmax=cmax,s=25,edgecolor=[.2,.2,.2])
cbar=fig.colorbar(d44ca,ax=AC,label='$\delta$44Ca', orientation='vertical',pad=.05,shrink=.4,ticks=[ round(a, 1) for a in np.linspace(cmin,cmax,7)])
d44ca=AC.scatter(BattleshipWash.d13c,BattleshipWash.SAMP_HEIGHT-240.0,c=BattleshipWash.d44ca,cmap=viridis,s=25,edgecolor=[.2,.2,.2],vmin=cmin,vmax=cmax)
#cbar=fig.colorbar(d44ca,ax=AC,label='$\delta$44Ca', orientation='vertical',pad=.01,shrink=.25,ticks=[ round(a, 1) for a in np.linspace(cmin,cmax,7)])
#fig.savefig('CaData.pdf', format='pdf', dpi=300)
Xplt = plt.subplot(gs[0,1])
Xplt2 = plt.subplot(gs[1,1])
Xplt.plot(mesh.printBox('rock','d13c')[1,:],mesh.printBox('rock','d44ca')[1,:],color=plt.rcParams['axes.color_cycle'][0])
Xplt.plot(ArrowCanyon.d13c,ArrowCanyon.d44ca,markeredgecolor='none',linestyle='none',marker='o',color=plt.rcParams['axes.color_cycle'][1])
Xplt2.plot(mesh.printBox('rock','d18o')[1,:],mesh.printBox('rock','d44ca')[1,:],color=plt.rcParams['axes.color_cycle'][0])
Xplt2.plot(ArrowCanyon.d18o,ArrowCanyon.d44ca,markeredgecolor='none',linestyle='none',marker='o',color=plt.rcParams['axes.color_cycle'][1])
Xplt.set_xlabel('$\delta$13C')
Xplt.set_ylabel('$\delta$44Ca')
Xplt.set_xlim(-8,3.0)
Xplt2.set_xlabel('$\delta$18O')
Xplt2.set_xlim(-8,0.0)
Xplt2.set_ylabel('$\delta$44Ca')
def __init__(self, encoder):
self.encoder = encoder
self.sensor = []
self.encoding = []
self.steer = []
self.reward = []
self.value = []
self.qValues = defaultdict(lambda: [])
self.bestAction = []
import matplotlib.pyplot as plt
self.plt = plt
import matplotlib.cm as cm
self.cm = cm
from pylab import rcParams
rcParams.update({'figure.figsize': (6, 9)})
# rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
def __init__(self, encoder):
self.encoder = encoder
self.sensor = []
self.encoding = []
self.steer = []
self.reward = []
self.value = []
self.qValues = defaultdict(lambda: [])
self.bestAction = []
import matplotlib.pyplot as plt
self.plt = plt
import matplotlib.cm as cm
self.cm = cm
from pylab import rcParams
rcParams.update({'figure.figsize': (6, 9)})
# rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
def plot6_1():
if paper:
fig_width = 3.0 # width in inches
fig_height = 4.0 # height in inches
params = {
'axes.titlesize': 8,
'axes.labelsize': 6,
'text.fontsize': 6,
'legend.fontsize': 5,
'xtick.labelsize': 6,
'ytick.labelsize': 6,
'figure.figsize': [fig_width, fig_height]
}
rcParams.update(params)
else:
fig_width = 6.15 # width in inches
fig_height = 5.0 # height in inches
params = {'figure.figsize': [fig_width, fig_height]}
rcParams.update(params)
re_mean = mmread("./out/ex6_1/ex6_1_rotherev_reward_mean.mtx")
sre_mean = mmread("./out/ex6_1/ex6_1_statefulre_reward_mean.mtx")
q_mean = mmread("./out/ex6_1/ex6_1_q_reward_mean.mtx")
rewards = [(re_mean, None, None, None, "Roth-Erev"),
(q_mean, None, None, None, "Q-Learning"),
(sre_mean, None, None, None, "Stateful RE")]
figure()
plot_agents(rewards,
r"Reward (\verb+$+)",
agents=[0, 3],
fmt=["w^", "wo", "kp"])
if tex:
if paper:
subplots_adjust(bottom=0.07, top=0.94, hspace=0.25)
else:
subplots_adjust(hspace=0.3)
savefig('./out/ex6_1/fig6_1.pdf')
else:
savefig('./out/ex6_1/fig6_1.png')
def getErrorbarFigures(title, X, Ys, stdDevs, plotTitles, xAxisLabel, yAxisLabels, gridFormat):
"""
Plots the specified data and saves specified plot to file
"""
rcParams["figure.figsize"] = 15, 15
fig = plt.figure()
fig.suptitle(title)
fig.subplots_adjust(left=None, right=None, bottom=None, top=None, wspace=None, hspace=0.35)
plt.ion()
plt.show()
rcParams.update({"font.size": 12})
for i, y in enumerate(Ys):
ax = fig.add_subplot(gridFormat + 1 + i)
ax.set_title(plotTitles[i])
ax.set_xlabel(xAxisLabel)
ax.set_ylabel(yAxisLabels[i])
ax.axis([0, max(X) + 10, 0, 20])
ax.errorbar(X, y, stdDevs[i])
return fig
def scl_shocktube(P, x=(0,1), **kwargs):
from pylab import sqrt, linspace, subplot, plot, text, xlabel, figure, show
from pylab import subplots_adjust, setp, gca, LinearLocator, rcParams, legend
rho = P[:,0]
plot_args = { }
plot_args['marker'] = kwargs.get('marker', 'o')
plot_args['c' ] = kwargs.get('c' , 'k')
plot_args['mfc' ] = kwargs.get('mfc' , 'None')
plot_args.update(kwargs)
rcParams.update({'axes.labelsize':16, 'ytick.major.pad':8})
X = linspace(x[0],x[1],P.shape[0])
ax = subplot(1,1,1)
plot(X,rho, **plot_args)
text(0.9,0.85, r"$\rho$", transform = ax.transAxes, fontsize=20)
if 'label' in plot_args: legend(loc='upper left')
def set_plot_style_for_paper():
params = {
'axes.labelsize': 18,
'font.size': 18,
'legend.fontsize': 18,
'xtick.labelsize': 18,
'ytick.labelsize': 18,
'text.usetex': True,
#'figure.figsize': [4.5, 4.5],
'figure.facecolor': 'w',
'figure.edgecolor': 'w',
'axes.facecolor': 'w',
'axes.edgecolor': 'gray',
'savefig.facecolor': 'w',
'savefig.edgecolor': 'g',
'savefig.pad_inches': 0.1,
'savefig.transparent': True,
'axes.titlepad': 20,
'axes.titlesize': 19
}
rcParams.update(params)
def graphHL(self, styles):
"""
Use matplotlib to plot error rates. styles is a list of line styles. Length of styles needs to be
equal to length of rangeHiddenUnits (One for every level of hidden units tested).
"""
if self.rangeLearningRate == None or self.errorsHL == None:
raise ValueError, 'Must call calcHAndL before calling graphHL'
if len(styles) < len(self.rangeHiddenUnits):
raise ValueError, 'Must have one style for every level of hidden units tested'
rcParams.update(self._PRAMS)
for i,h in enumerate(self.rangeHiddenUnits):
plot(self.rangeLearningRate, self.errorsHL[i], styles[i], label="h="+str(h), linewidth=2)
xlabel('Learning Rate')
ylabel('Error')
title('Error for Hidden Units and Learning Rate')
legend()
show()
def __init__(self, field, vehicle, scorer, model):
self.field = field
self.vehicle = vehicle
self.scorer = scorer
self.model = model
import matplotlib.pyplot as plt
self.plt = plt
import matplotlib.cm as cm
self.cm = cm
from pylab import rcParams
rcParams.update({'figure.figsize': (8, 14)})
rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
self.plt.ion()
self.plt.show()
self.positions = []
self.sensorValues = []
self.sensorNoiseAmounts = []
self.motorValues = []
self.motorNoiseAmounts = []
self.scores = []
self.goalValues = []
def do_band(gal, band, pos):
cmd = 'select a.objid, a.petroR50_{band}, a.petromag_{band}-a.extinction_{band}, a.petroR50_r/0.396, b.BT, b.m_tot-a.extinction_{band}, m.probaE from {band}_band_serexp as b, CAST as a, M2010 as m where a.galcount = m.galcount and a.galcount = b.galcount and a.galcount = {galcount};'.format(band=band, galcount=gal)
print cmd
objid, petrorad, petromag, hrad,BT, mag_tot, probaE = cursor.get_data(cmd)
objid = objid[0]
petromag = petromag[0]
petrorad = petrorad[0]
hrad = hrad[0]
BT = BT[0]
mag_tot = mag_tot[0]
probaE = probaE[0]
fig.add_subplot(1,3,pos)
pticks = pub_plots(xmaj = 10, xmin = 5, xstr = '%d', ymaj = 10, ymin = 5, ystr = '%d')
MatPlotParams = {'xtick.major.pad' :10, 'ytick.major.pad' :10,'xtick.minor.pad' :10, 'ytick.minor.pad' :10, 'axes.labelsize': 8, 'xtick.labelsize': 8, 'ytick.labelsize': 8}
rcParams.update(MatPlotParams)
data = load_image('/media/SDSS2/fit_catalog/data/{band}/{folder}/{galcount}_{band}_stamp.fits'.format(band=band, galcount='%08d' %gal, folder = '%04d' %((gal-1)/250 +1)))
data = resize_image(data, hrad)
data = np.log10(data)
zmin, zmax = make_panel(data, pticks, color = cm.gray_r, zmin=2.0*np.nanmin(data)-np.percentile(np.extract(np.isnan(data)==0,data), 95.0))
pl.title('{band}-band'.format(band=band),fontsize=12)
ax = pl.gca()
pl.text(0.05, 0.9, 'm$_{petro}$=%3.1f' %(petromag),
fontsize=fsize,
horizontalalignment='left', verticalalignment='center',
transform=ax.transAxes)
pl.text(0.95, 0.9, 'r$_{petro}$=%4.2f"' %(petrorad),
fontsize=fsize,
horizontalalignment='right', verticalalignment='center',
transform=ax.transAxes)
pl.text(0.05, 0.1, 'galnum=%s' %gal, fontsize=fsize,
horizontalalignment='left', verticalalignment='center',
transform=ax.transAxes)
pl.text(0.95, 0.1, 'P(Early)=%03.2f' %(probaE),
fontsize=fsize,
horizontalalignment='right', verticalalignment='center',
transform=ax.transAxes)
return
def __init__(self, field, vehicle, scorer, model):
self.field = field
self.vehicle = vehicle
self.scorer = scorer
self.model = model
import matplotlib.pyplot as plt
self.plt = plt
import matplotlib.cm as cm
self.cm = cm
from pylab import rcParams
rcParams.update({'figure.figsize': (8, 14)})
rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
self.plt.ion()
self.plt.show()
self.positions = []
self.sensorValues = []
self.sensorNoiseAmounts = []
self.motorValues = []
self.motorNoiseAmounts = []
self.scores = []
self.goalValues = []
def SetForPng(proport=0.75, fig_width_pt=455.24, dpi=150, xylabel_fontsize=9,
leg_fontsize=8, text_fontsize=9, xtick_fontsize=7, ytick_fontsize=7):
"""
Set figure proportions
======================
"""
inches_per_pt = 1.0/72.27 # Convert pt to inch
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*proport # height in inches
fig_size = [fig_width,fig_height]
params = {
'axes.labelsize' : xylabel_fontsize,
'font.size' : text_fontsize,
'legend.fontsize' : leg_fontsize,
'xtick.labelsize' : xtick_fontsize,
'ytick.labelsize' : ytick_fontsize,
'figure.figsize' : fig_size,
'savefig.dpi' : dpi,
}
MPLclose()
rcdefaults()
rcParams.update(params)
def plot6_1():
if paper:
fig_width = 3.0 # width in inches
fig_height = 4.0 # height in inches
params = {'axes.titlesize': 8,
'axes.labelsize': 6,
'text.fontsize': 6,
'legend.fontsize': 5,
'xtick.labelsize': 6,
'ytick.labelsize': 6,
'figure.figsize': [fig_width, fig_height]}
rcParams.update(params)
else:
fig_width = 6.15 # width in inches
fig_height = 5.0 # height in inches
params = {'figure.figsize': [fig_width, fig_height]}
rcParams.update(params)
re_mean = mmread("./out/ex6_1/ex6_1_rotherev_reward_mean.mtx")
sre_mean = mmread("./out/ex6_1/ex6_1_statefulre_reward_mean.mtx")
q_mean = mmread("./out/ex6_1/ex6_1_q_reward_mean.mtx")
rewards = [
(re_mean, None, None, None, "Roth-Erev"),
(q_mean, None, None, None, "Q-Learning"),
(sre_mean, None, None, None, "Stateful RE")
]
figure()
plot_agents(rewards, r"Reward (\verb+$+)", agents=[0, 3],
fmt=["w^", "wo", "kp"])
if tex:
if paper:
subplots_adjust(bottom=0.07, top=0.94, hspace=0.25)
else:
subplots_adjust(hspace=0.3)
savefig('./out/ex6_1/fig6_1.pdf')
else:
savefig('./out/ex6_1/fig6_1.png')
def plot6_3():
if paper:
fig_width = 3.0 # width in inches
fig_height = 4.0 # height in inches
params = {
'axes.titlesize': 8,
'axes.labelsize': 6,
'text.fontsize': 6,
'legend.fontsize': 6,
'xtick.labelsize': 6,
'ytick.labelsize': 6,
'figure.figsize': [fig_width, fig_height]
}
rcParams.update(params)
else:
fig_width = 6.15 # width in inches
fig_height = 5.0 # height in inches
params = {'figure.figsize': [fig_width, fig_height]}
rcParams.update(params)
q_mean = mmread("./out/ex6_3/ex6_3_q_reward_mean.mtx")
enac_mean = mmread("./out/ex6_3/ex6_3_enac_reward_mean.mtx")
rewards = [(q_mean, None, None, None, "Q-Learning"),
(enac_mean, None, None, None, "ENAC")]
figure()
plot_agents(rewards, r"Reward (\verb+$+)", agents=[0, 3], fmt=["wo", "kv"])
subplots_adjust(hspace=0.3)
if tex:
if paper:
subplots_adjust(bottom=0.07, top=0.94, hspace=0.25)
else:
subplots_adjust(hspace=0.3)
savefig('./out/ex6_3/fig6_3.pdf')
else:
savefig('./out/ex6_3/fig6_3.png')
def plot6_3():
if paper:
fig_width = 3.0 # width in inches
fig_height = 4.0 # height in inches
params = {'axes.titlesize': 8,
'axes.labelsize': 6,
'text.fontsize': 6,
'legend.fontsize': 6,
'xtick.labelsize': 6,
'ytick.labelsize': 6,
'figure.figsize': [fig_width, fig_height]}
rcParams.update(params)
else:
fig_width = 6.15 # width in inches
fig_height = 5.0 # height in inches
params = {'figure.figsize': [fig_width, fig_height]}
rcParams.update(params)
q_mean = mmread("./out/ex6_3/ex6_3_q_reward_mean.mtx")
enac_mean = mmread("./out/ex6_3/ex6_3_enac_reward_mean.mtx")
rewards = [
(q_mean, None, None, None, "Q-Learning"),
(enac_mean, None, None, None, "ENAC")
]
figure()
plot_agents(rewards, r"Reward (\verb+$+)", agents=[0, 3], fmt=["wo", "kv"])
subplots_adjust(hspace=0.3)
if tex:
if paper:
subplots_adjust(bottom=0.07, top=0.94, hspace=0.25)
else:
subplots_adjust(hspace=0.3)
savefig('./out/ex6_3/fig6_3.pdf')
else:
savefig('./out/ex6_3/fig6_3.png')
def SetForPng(proport=0.75, fig_width_pt=455.24, dpi=150, xylabel_fs=9, leg_fs=8, text_fs=9, xtick_fs=7, ytick_fs=7):
"""
Set figure proportions
======================
"""
inches_per_pt = 1.0 / 72.27 # Convert pt to inch
fig_width = fig_width_pt * inches_per_pt # width in inches
fig_height = fig_width * proport # height in inches
fig_size = [fig_width, fig_height]
params = {
"axes.labelsize": xylabel_fs,
"font.size": text_fs,
"legend.fontsize": leg_fs,
"xtick.labelsize": xtick_fs,
"ytick.labelsize": ytick_fs,
"figure.figsize": fig_size,
"savefig.dpi": dpi,
}
# utl.Ff(&bb, "SetFontSize(%s)\n", args)
# utl.Ff(&bb, "rcParams.update({'savefig.dpi':%d})\n", dpi)
MPLclose()
rcdefaults()
rcParams.update(params)
def PlotProbBins(X, P):
"""
PlotProbBins plots probabilities bins
X -- population
P -- probabilities
"""
rcParams.update({'figure.figsize':[800/72.27,200/72.27]})
x0, Tk = 0.0, [0.0]
for i in range(len(X)):
gca().add_patch(Rectangle([x0, 0], P[i], 0.2, color='#d5e7ed', ec='black', clip_on=0))
ha = 'center'
if i==len(X)-1: ha = 'left' # last one
text(x0+P[i]/2.0, 0.1, '%.1f'%X[i], ha=ha)
x0 += P[i]
Tk.append(x0)
xticks(Tk, ['%.2f'%v for v in Tk])
axis('equal')
gca().get_yaxis().set_visible(False)
for dir in ['left', 'right', 'top']:
gca().spines[dir].set_visible(False)
xlabel('cumulated probability')
grid()
axis([0, 1, 0, 0.2])
def SetForEps(
proport=0.75,
fig_width_pt=455.24,
xylabel_fs=9,
leg_fs=8,
text_fs=9,
xtick_fs=7,
ytick_fs=7,
text_usetex=True,
mplclose=True,
):
"""
Set figure proportions
======================
"""
# fig_width_pt = 455.24411 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0 / 72.27 # Convert pt to inch
fig_width = fig_width_pt * inches_per_pt # width in inches
fig_height = fig_width * proport # height in inches
fig_size = [fig_width, fig_height]
# params = {'mathtext.fontset':'stix', # 'cm', 'stix', 'stixsans', 'custom'
params = {
"backend": "ps",
"axes.labelsize": xylabel_fs,
"font.size": text_fs,
"legend.fontsize": leg_fs,
"xtick.labelsize": xtick_fs,
"ytick.labelsize": ytick_fs,
"text.usetex": text_usetex, # very IMPORTANT to avoid Type 3 fonts
"ps.useafm": True, # very IMPORTANT to avoid Type 3 fonts
"pdf.use14corefonts": True, # very IMPORTANT to avoid Type 3 fonts
"figure.figsize": fig_size,
}
if mplclose:
MPLclose()
rcdefaults()
rcParams.update(params)
def __init__(self, tm):
self.tm = tm
self.overlaps = []
import matplotlib.pyplot as plt
self.plt = plt
import matplotlib.cm as cm
self.cm = cm
from pylab import rcParams
rcParams.update({'figure.figsize': (6, 9)})
rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pylab import rcParams
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
rcParams.update({'figure.autolayout': True})
data = pd.read_csv("./datasets/telecom churn.csv")
data.head()
label = data["Churn"].value_counts(sort=True).index.tolist()
size = data["Churn"].value_counts(sort=True)
'''
./results/plt-churn_percentage.png
'''
plt.pie(size, autopct="%1.1f%%", shadow=True, startangle=55, labels=label, explode=(0.1, 0.1))
plt.title("Churn%")
plt.show()
# dropping irrelevant columns
data.drop(['customerID'], axis=1, inplace=True)
data.isnull().any() # all False indicating no null values
data['TotalCharges'] = pd.to_numeric(data['TotalCharges']) # error
type=str)
parser.add_argument('-w', '--window', type=int, default=100)
parser.add_argument('-n', '--num', type=int, default=None)
parser.add_argument('-t', '--training-hide', type=int, nargs='+')
parser.add_argument('-g', '--graph-labels', type=str, nargs='+')
parser.add_argument('-s', '--size-of-line', type=float, nargs='+')
parser.add_argument('-l', '--legend-position', type=int, default=4)
parser.add_argument('-f', '--full', action='store_true')
parser.add_argument('-o', '--output', type=str, default=None)
suite = PyExperimentSuite()
args = parser.parse_args()
from pylab import rcParams
rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
rcParams.update({'ytick.labelsize': 8})
rcParams.update({'figure.figsize': (12, 6)})
rcParams.update({'pdf.fonttype': 42})
experiments = args.experiments
for i, experiment in enumerate(experiments):
iteration = suite.get_history(experiment, 0, 'iteration')
predictions = suite.get_history(experiment, 0, 'predictions')
truth = suite.get_history(experiment, 0, 'truth')
train = suite.get_history(experiment, 0, 'train')
resets = None if args.full else suite.get_history(
experiment, 0, 'reset')
from __future__ import print_function, division
from nilmtk import DataSet, TimeFrame, MeterGroup
import plot_config
import seaborn as sns
from matplotlib.dates import DateFormatter, HourLocator
from datetime import timedelta
import matplotlib.pyplot as plt
import pytz
import pandas as pd
from os.path import join
from pylab import rcParams
rcParams.update({'figure.figsize': plot_config._mm_to_inches(180, 100)})
UNIT = 'kW'
dataset = DataSet('/data/mine/vadeec/merged/ukdale.h5')
TZ_STRING = dataset.metadata['timezone']
TZ = pytz.timezone(TZ_STRING)
elec = dataset.buildings[1].elec
submeters = elec.meters_directly_downstream_of_mains()
# Select appliances used in top K plot
APPLIANCES = [
'fridge freezer', 'HTPC', 'dish washer', 'washer dryer', 'kettle'
]
selected_meters = [submeters[appliance] for appliance in APPLIANCES]
remainder = []
for meter in submeters.meters:
for appliance in APPLIANCES:
plt.axis('off')
# Ground Truth
plt.subplot(1, 3, 2)
plt.imshow(a_i.reshape(patch_size,patch_size), cmap='Greys')
plt.axis('off')
# Denoised Image
plt.subplot(1, 3, 3)
plt.imshow(output_any_image.reshape(patch_size,patch_size), cmap='Greys')
plt.axis('off')
plt.show()
'''
if epoch%100 == 0 :
print('Epoch', epoch, '/', hm_epochs, 'loss:',epoch_loss)
x = range(5000,5000*(len(PSNR_0)+1),5000)
y0 = PSNR_0
y1 = PSNR_1
rcParams['figure.figsize'] = 8,8
rcParams.update({'font.size': 12})
plt.plot(x,y0,'b',label='Lena',marker='^')
plt.plot(x,y1,'r',label='Man',marker='D')
plt.xlabel('Epochs')
plt.ylabel('PSNR(dB)')
plt.title('MLP(multilayer perceptron)')
plt.legend(loc='upper right')
plt.show()
def plot_bar(data_fh, target, xlabel, ylabel, zlabel, title, x_label, y_label,
x_order, y_order, fig_width, fig_height, fontsize,
xlabel_rotation, category, colours):
'''
xlabel: groups on x axis
ylabel: colours
'''
logging.info('starting...')
import matplotlib.style
matplotlib.style.use('seaborn')
included = total = 0
results = {}
xvals = set()
yvals = set()
max_zval = 0.0
categories = {}
for row in csv.DictReader(data_fh, delimiter='\t'):
try:
included += 1
xval = row[xlabel] # group axis value
yval = row[ylabel] # sub-group axis value
xvals.add(xval)
yvals.add(yval)
zval = float(row[zlabel])
max_zval = max(max_zval, zval)
xy = '{},{}'.format(xval, yval)
results[xy] = zval
logging.debug('Added %s = %f', xy, zval)
if category is not None:
categories[xy] = row[category]
except:
logging.debug('Failed to include %s', row)
total += 1
logging.info('finished reading %i of %i records with max_zval %.2f',
included, total, max_zval)
if len(results) == 0:
logging.warn('No data to plot')
return
if x_order is None:
xvals = sorted(list(xvals)) # groups
else:
xvals = x_order # groups
if y_order is None:
yvals = sorted(list(yvals)) # sub-groups
else:
yvals = y_order
logging.debug('xvals %s yvals %s', xvals, yvals)
#fig, ax = plt.subplots()
#fig_width = min(18, max(9, len(xvals) * len(yvals)))
fig = plt.figure(figsize=(fig_width, fig_height))
rcParams.update({'font.size': fontsize})
ax = fig.add_subplot(111)
width = fig_width / len(xvals) / len(yvals)
ind = np.arange(len(xvals)) * fig_width / len(
xvals) # the x locations for the groups
logging.info('ind is %s, width is %f fig_width is %f', ind, width,
fig_width)
for idx in range(len(yvals)): # each yval
offset = idx * width * 0.9 - (len(yvals) - 1) * width / 2
vals = [results['{},{}'.format(x, yvals[idx])]
for x in xvals] # each xval with that yval
logging.debug('adding values %s for %s at %s', vals, yvals[idx],
ind + offset)
if category is None:
rects = ax.bar(ind + offset, vals, width * 0.85, label=yvals[idx])
else:
rects = ax.bar(ind + offset, vals, width * 0.85)
for rect, val in zip(rects, xvals):
height = rect.get_height()
if height < 0.01:
annot = '{:.3e}'.format(height)
else:
annot = '{:.2f}'.format(height)
ax.annotate(
annot,
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # use 3 points offset
textcoords="offset points", # in both directions
ha='center',
va='bottom')
if category is not None:
label = '{} {}'.format(
categories['{},{}'.format(val, yvals[idx])], yvals[idx])
rect.set_label(label)
if colours is not None:
for colour in colours:
cat, col = colour.split('=')
if cat == label:
rect.set_color(col)
# Add some text for labels, title and custom x-axis tick labels, etc.
if y_label is not None:
ax.set_ylabel(y_label)
if x_label is not None:
ax.set_xlabel(x_label)
ax.set_title(title)
ax.set_xticks(ind)
ax.set_xticklabels(xvals, rotation=xlabel_rotation)
#ax.legend(loc='upper right')
# place legend at right based on https://stackoverflow.com/questions/10101700/moving-matplotlib-legend-outside-of-the-axis-makes-it-cutoff-by-the-figure-box/10154763#10154763
handles, labels = ax.get_legend_handles_labels()
labels_seen = set()
labels_u = []
handles_u = []
for handle, label in sorted(zip(handles, labels),
key=lambda pair: pair[1]):
if label in labels_seen:
continue
labels_seen.add(label)
labels_u.append(label)
handles_u.append(handle)
lgd = ax.legend(handles_u,
labels_u,
loc='upper left',
bbox_to_anchor=(1.01, 1.0),
borderaxespad=0)
lgd.get_frame().set_edgecolor('#000000')
#fig = plt.figure(figsize=(figsize, 1 + int(figsize * len(yvals) / len(xvals))))
#ax = fig.add_subplot(111)
logging.info('done processing %i of %i', included, total)
plt.tight_layout()
plt.savefig(target)
matplotlib.pyplot.close('all')
def plot(title,
results,
sort_by='total',
fract_of=False,
y_label=False,
num_to_plot=7,
significance_level=0.05,
multiplier=100,
yearspan=False,
proj63=5,
justyears=False,
csvmake=False,
x_label=False,
legend_p=False,
legend_totals=False,
log=False,
figsize=11,
save=False,
only_below_p=False,
skip63=False,
projection=True):
"""
Visualise interrogator() results, optionally generating a csv as well.
Parameters
----------
title : string
Chart title
results : list
interrogator() results or totals (deaults to results)
sort_by : string
'total': sort by most frequent
'increase': calculate linear regression, sort by steepest up slope
'decrease': calculate linear regression, sort by steepest down slope
'static': calculate linear regression, sort by least slope
fract_of : list
measure results as a fraction (default: as a percentage) of this list.
usually, use interrogator() totals
multiplier : int
mutliply results list before dividing by fract_of list
Default is 100 (for percentage), can use 1 for ratios
y_label : string
text for y-axis label (default is 'Absolute frequency'/'Percentage')
X_label : string
text for x-axis label (default is 'Group'/'Year')
num_to_plot : int
How many top entries to show
significance_level : float
If using sort_by, set the p threshold (default 0.05)
only_below_p : Boolean
Do not plot any results above p value
yearspan : list with two ints
Get only results between the specified ints
justyears : list of ints
Get only results from the listed subcorpora
csvmake : True/False/string
Generate a CSV of plotted and all results with string as filename
If True, 'title' string is used
legend_totals : Boolean
Show total frequency of each result, or overall percentage if fract_of
legend_p : Boolean
Show p-value for slope when using sort_by
log : False/'x'/'y'/'x, y'
Use logarithmic axes
figsize = int
Size of image
save = True/False/string
Generate save image with string as filename
If True, 'title' string is used for name
NYT-only parameters
-----
skip63 : boolean
Skip 1963 results (for NYT investigation)
projection : boolean
Project 1963/2014 results (for NYT investigation)
proj63 : int
The amount to project 1963 results by
Example
-----
from corpkit import interrogator, plotter
corpus = 'path/to/corpus'
adjectives = interrogator(corpus, 'words', r'/JJ.?/ < __')
plotter('Most common adjectives', adjectives.results, fract_of = adjectives.totals,
csvmake = True, legend_totals = True)
"""
import os
import warnings
import copy
from time import localtime, strftime
import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.ticker import MaxNLocator, ScalarFormatter
import pylab
from pylab import rcParams
try:
get_ipython().getoutput()
except TypeError:
have_ipython = True
except NameError:
import subprocess
have_ipython = False
try:
from IPython.display import display, clear_output
except ImportError:
pass
from corpkit.query import check_dit, check_pytex, check_tex
from corpkit.edit import resorter, mather
# setup:
# size:
rcParams['figure.figsize'] = figsize, figsize / 2
#font
rcParams.update({'font.size':
(figsize / 2) + 7}) # half your size plus seven
# check what we're doing here.
have_python_tex = check_pytex()
on_cloud = check_dit()
have_tex = check_tex(have_ipython=have_ipython)
def skipper(interrogator_list):
"""Takes a list and returns a version without 1963"""
skipped = []
skipped.append(interrogator_list[0]) # append word
for item in interrogator_list[1:]:
if type(item) != unicode and type(item) != str and item[0] != 1963:
skipped.append(item)
return skipped
def yearskipper(interrogator_list, justyears):
"""Takes a list and returns only results from the years listed in justyears"""
skipped = []
skipped.append(interrogator_list[0]) # append word
for item in interrogator_list[1:]:
if type(item) != unicode and type(item) != str:
for year in justyears:
if item[0] == year:
skipped.append(item)
return skipped
def yearspanner(interrogator_list, yearspan):
"""Takes a list and returns results from between the first and last year in yearspan"""
skipped = [interrogator_list[0]] # append word
for item in interrogator_list[1:]:
if type(item) != unicode and type(item) != str:
if item[0] >= yearspan[0]:
if item[0] <= yearspan[-1] + 1:
skipped.append(item)
return skipped
def projector(interrogator_list):
"""Takes a list and returns a version with projections"""
projected = []
projected.append(interrogator_list[0]) # append word
for item in interrogator_list[1:]:
if type(item) != str and type(item) != str and item[0] == 1963:
newtotal = item[1] * proj63
datum = [item[0], newtotal]
projected.append(datum)
elif type(item) != str and type(item) != str and item[0] == 2014:
newtotal = item[1] * 1.37
datum = [item[0], newtotal]
projected.append(datum)
else:
projected.append(item)
return projected
def csvmaker(csvdata, csvalldata, csvmake):
"""Takes whatever ended up getting plotted and puts it into a csv file"""
# now that I know about Pandas, I could probably make this much less painful.
csv = []
yearlist = []
# get list of years
for entry in csvdata[0]:
if type(entry) == list:
yearlist.append(str(entry[0]))
# make first line
csv.append(title)
# make the second line
years = ',' + ','.join(yearlist)
csv.append(years)
# for each word
for entry in csvdata:
csvline = []
csvcounts = []
csvline.append(entry[0]) # append word
for part in entry[1:]:
csvcounts.append(str(part[1])) # append just the count
counts = ','.join(csvcounts)
csvline.append(counts)
line = ','.join(csvline)
csv.append(line)
csv = '\n'.join(csv)
# now do all data
csvall = []
yearlist = []
# get list of years
for entry in csvalldata[0]:
if type(entry) == list:
yearlist.append(str(entry[0]))
# make first line
csvall.append(title)
# make the second line
years = ',' + ','.join(yearlist)
csvall.append(years)
# for each word
for entry in csvalldata:
csvallline = []
csvallcounts = []
csvallline.append(entry[0]) # append word
for part in entry[1:]:
csvallcounts.append(str(part[1])) # append just the count
counts = ','.join(csvallcounts)
csvallline.append(counts)
line = ','.join(csvallline)
csvall.append(line)
csvall = '\n'.join(csvall)
# write the csvall file?
if os.path.isfile(csvmake):
raise ValueError(
"CSV error: %s already exists in current directory. \
Move it, delete it, or change the name of the new .csv file."
% csvmake)
try:
fo = open(csvmake, "w")
except IOError:
print "Error writing CSV file."
fo.write('Plotted results:\n'.encode("UTF-8"))
fo.write(csv.encode("UTF-8"))
fo.write('\n\nAll results:\n'.encode("UTF-8"))
fo.write(csvall.encode("UTF-8"))
fo.close()
time = strftime("%H:%M:%S", localtime())
print time + ": " + csvmake + " written to currect directory."
##################################################################
# check for tex and use it if it's there
if have_tex:
rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
rc('text', usetex=True)
#image directory
if have_python_tex:
imagefolder = '../images'
else:
imagefolder = 'images'
# Use .results branch if branch is unspecified
if isinstance(results, tuple) is True:
warnings.warn('\nNo branch of results selected. Using .results ... ')
results = results.results
if only_below_p:
if sort_by == 'static':
warnings.warn(
'\nStatic trajectories will confirm the null hypothesis, so it might '
+
'not be helpful to use both the static and only_below_p options together.'
)
if sort_by == 'total' or sort_by == 'name':
warnings.warn(
"\nP value has not been calculated. No entries will be excluded"
)
# cut short to save time if later results aren't useful
if csvmake or sort_by != 'total':
cutoff = len(results)
else:
cutoff = num_to_plot
# if plotting one entry/a totals list, wrap it in another list
if type(results[0]) == unicode or type(results[0]) == str:
legend = False
alldata = [copy.deepcopy(results)][:cutoff]
num_to_plot = 1
else:
legend = True
alldata = copy.deepcopy(results[:cutoff])
# determine if no subcorpora and thus barchart
if len(results[0]) == 3 or len(results[0]) == 4:
barchart = True
else:
barchart = False
# if no x_label, guess 'year' or 'group'
if x_label:
x_lab = x_label
else:
if not barchart:
check_x_axis = alldata[0] # get first entry
check_x_axis = check_x_axis[
1] # get second entry of first entry (year, count)
if 1500 < check_x_axis[0] < 2050:
x_lab = 'Year'
else:
x_lab = 'Group'
else:
x_lab = False
# select totals if no branch selected
if fract_of:
if isinstance(fract_of, tuple) is True:
warnings.warn(
'\nNo branch of fract_of selected. Using .totals ... ')
fract_of = fract_of.totals
# copy this, to be safe!
totals = copy.deepcopy(fract_of)
#use mather to make percentage results
fractdata = []
for entry in alldata:
fractdata.append(mather(entry, '%', totals, multiplier=multiplier))
alldata = copy.deepcopy(fractdata)
# sort_by with resorter
if sort_by != 'total':
do_stats = True
alldata = resorter(alldata,
sort_by=sort_by,
keep_stats=True,
only_below_p=only_below_p,
significance_level=significance_level,
skip63=skip63)
else:
do_stats = False
csvdata = []
csvalldata = []
final = []
colours = [
"#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a", "#a6cee3",
"#b2df8a", "#fb9a99", "#fdbf6f", "#cab2d6"
]
c = 0
if num_to_plot > len(alldata):
warnings.warn(
"There are not %d entries to show.\nPlotting all %d results..." %
(num_to_plot, len(alldata)))
if not csvmake:
cutoff = num_to_plot
if not barchart:
for index, entry in enumerate(alldata[:cutoff]):
# run called processes
if skip63:
entry = skipper(entry)
if yearspan:
entry = yearspanner(entry, yearspan)
if justyears:
entry = yearskipper(entry, justyears)
if projection:
if not fract_of:
entry = projector(entry)
# get word
word = entry[0]
if do_stats:
pval = entry[-1][3]
p_short = "%.4f" % pval
p_string = ' (p=%s)' % p_short
# remove stats, we're done with them.
entry.pop()
# get totals ... horrible code
total = 0
if fract_of:
if entry[-1][0] == 'Total':
num = entry[-1][1]
total = "%.2f" % num
#total = str(float(entry[-1][1]))[:5]
totalstring = ' (' + str(total) + '\%)'
else:
if entry[-1][0] == 'Total':
total = entry[-1][1]
totalstring = ' (n=%d)' % total
entry.pop() # get rid of total. good or bad?
csvalldata.append(entry)
if index < num_to_plot:
csvdata.append(entry)
toplot = []
xvalsbelow = []
yvalsbelow = []
xvalsabove = []
yvalsabove = []
d = 1 # first tuple, maybe not very stable
tups = len(
entry) - 2 # all tuples minus 2 (to skip totals tuple)
for _ in range(tups):
firstpart = entry[d] # first tuple
firstyear = firstpart[0]
nextpart = entry[d + 1]
nextyear = nextpart[0]
if nextyear - firstyear > 50: # change to 1 for nyt
xvalsbelow.append(firstpart[0])
yvalsbelow.append(firstpart[1])
xvalsbelow.append(nextpart[0])
yvalsbelow.append(nextpart[1])
else:
xvalsabove.append(firstpart[0])
yvalsabove.append(firstpart[1])
xvalsabove.append(nextpart[0])
yvalsabove.append(nextpart[1])
d += 1
if csvmake: # append exactly what was plotted...
csvdata.append(entry)
# do actual plotting
plt.plot(xvalsbelow, yvalsbelow, '--', color=colours[c])
plt.plot(xvalsabove,
yvalsabove,
'-',
label=word,
color=colours[c])
plt.plot(xvalsabove, yvalsabove, '.',
color=colours[c]) # delete for nyt
if c == 8:
c = 0 # unpythonic
c += 1
# old way to plot everything at once
#plt.plot(*zip(*toplot), label=word) # this is other projects...
#make legend
if legend:
lgd = plt.legend(bbox_to_anchor=(1.05, 1),
loc=2,
borderaxespad=0.,
fancybox=True,
framealpha=0.5)
elif barchart:
rcParams['figure.figsize'] = figsize, figsize / 2
cutoff = len(alldata)
import numpy as np
scores = [entry[1][1] for entry in alldata[:cutoff]]
ind = np.arange(cutoff) # the x locations for the groups
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(ind, scores, width, color="#1f78b4")
if len(results[0]) == 4:
compscores = [entry[2][1] for entry in alldata[:cutoff]]
rects2 = ax.bar(ind + width, compscores, width, color="#33a02c")
# add some text for labels, title and axes ticks
ax.set_xticks(ind + width)
# get labels
labels = [entry[0] for entry in alldata[:cutoff]]
longest = len(max(labels, key=len))
if longest > 7:
if figsize < 20:
if num_to_plot > 6:
ax.set_xticklabels(labels, rotation=45)
else:
ax.set_xticklabels(labels)
# rotate the labels if they're long:
#def autolabel(rects):
# attach some text labels
#for rect in rects:
#height = rect.get_height()
#ax.text(rect.get_x()+rect.get_width()/2., 1.0*height, '%d'%int(height),
#ha='center', va='bottom')
#autolabel(rects1)
#if len(results[0]) == 4:
#autolabel(rects2)
legend_labels = [alldata[0][1][0], alldata[0][2][0]]
ax.legend((rects1[0], rects2[0]), legend_labels)
# make axis labels
if x_lab:
plt.xlabel(x_lab)
if not y_label:
#print "Warning: no name given for y-axis. Using default."
if fract_of:
y_label = 'Percentage'
if not fract_of:
y_label = 'Total frequency'
plt.ylabel(y_label)
pylab.title(title)
if not barchart:
plt.gca().get_xaxis().set_major_locator(MaxNLocator(integer=True))
if log == 'x':
plt.xscale('log')
plt.gca().get_xaxis().set_major_formatter(ScalarFormatter())
elif log == 'y':
plt.yscale('log')
plt.gca().get_yaxis().set_major_formatter(ScalarFormatter())
elif log == 'x, y':
plt.xscale('log')
plt.gca().get_xaxis().set_major_formatter(ScalarFormatter())
plt.yscale('log')
plt.gca().get_yaxis().set_major_formatter(ScalarFormatter())
else:
plt.ticklabel_format(useOffset=False, axis='x', style='plain')
plt.grid()
fig1 = plt.gcf()
if not have_python_tex:
plt.show()
def urlify(s):
import re
s = s.lower()
s = re.sub(r"[^\w\s]", '', s)
s = re.sub(r"\s+", '-', s)
return s
if save:
if type(save) == str:
savename = os.path.join(imagefolder, urlify(save) + '.png')
else:
savename = os.path.join(imagefolder, urlify(title) + '.png')
if legend and not barchart:
fig1.savefig(savename,
bbox_extra_artists=(lgd, ),
bbox_inches='tight',
dpi=150,
transparent=True)
else:
fig1.savefig(savename, dpi=150, transparent=True)
time = strftime("%H:%M:%S", localtime())
if os.path.isfile(savename):
print time + ": " + savename + " created."
else:
raise ValueError("Error making %s." % savename)
if csvmake:
if type(csvmake) == bool:
csvmake = urlify(title) + '.csv'
csvmaker(csvdata, csvalldata, csvmake)
def plot6_2():
if paper:
fig_width = 3.0 # width in inches
fig_height = 5.5 # height in inches
params = {
'axes.titlesize': 6,
'axes.labelsize': 6,
'text.fontsize': 6,
'legend.fontsize': 5,
'xtick.labelsize': 6,
'ytick.labelsize': 6,
'figure.figsize': [fig_width, fig_height]
}
rcParams.update(params)
q1 = mmread("./out/ex6_2/ex6_2-1_q_reward_mean.mtx")
reinforce1 = mmread("./out/ex6_2/ex6_2-1_reinforce_reward_mean.mtx")
enac1 = mmread("./out/ex6_2/ex6_2-1_enac_reward_mean.mtx")
# q2 = mmread("./out/ex6_2/ex6_2-2_q_reward_mean.mtx")
reinforce2 = mmread("./out/ex6_2/ex6_2-2_reinforce_reward_mean.mtx")
enac2 = mmread("./out/ex6_2/ex6_2-2_enac_reward_mean.mtx")
maxSteps = 24
nplots = 4
fmt = ["wo", "ks", "kv"]
x = arange(0.0, maxSteps, 1.0)
def plot62(results, i, n):
ax = subplot(nplots, 1, n)
if paper:
msize = 3
else:
msize = 5
for k, (r, lab) in enumerate(results):
plot(x,
r[i, :],
fmt[k],
linestyle="None",
markersize=msize,
label=lab)
ax.ticklabel_format(style='sci', scilimits=(0, 0), axis='y')
xlim((0, 23))
if paper:
locator = FixedLocator(range(0, 24, 2))
else:
locator = FixedLocator(range(0, 24))
ax.xaxis.set_major_locator(locator) #minor x-axis ticks
ylabel(r"Reward (\verb+$+)")
l = legend(loc="upper left")
l.get_frame().set_linewidth(0.5)
results1 = [(q1, "Q-learning"), (reinforce1, "REINFORCE"), (enac1, "ENAC")]
results2 = [(q1, "Q-learning"), (reinforce2, "REINFORCE"), (enac2, "ENAC")]
figure()
plot62(results1, i=1, n=1)
title("Agent 1 (demand forecast only)")
plot62(results2, i=1, n=2)
title("Agent 1 (demand and bus voltage)")
# xlabel("Hour")
# if tex:
# savefig('./out/ex6_2/fig6_2_agent1.pdf')
# figure()
plot62(results1, i=3, n=3)
title("Agent 4 (demand forecast only)")
plot62(results2, i=3, n=4)
title("Agent 4 (demand and bus voltage)")
xlabel("Hour")
# if tex:
# savefig('./out/ex6_2/fig6_2_agent4.pdf')
subplots_adjust(hspace=0.3)
if tex:
if paper:
subplots_adjust(bottom=0.05, top=0.96)
savefig('./out/ex6_2/fig6_2.pdf')
else:
savefig('./out/ex6_2/fig6_2.png')
FONTSIZE = 8
params = {
'figure.figsize':
_mm_to_inches(180, 80), # 180 or 88mm wide for Scientific Data
'axes.labelsize': FONTSIZE, # FONTSIZE for x and y labels (was FONTSIZE)
'axes.titlesize': FONTSIZE,
'text.fontsize': FONTSIZE, # was FONTSIZE
'legend.fontsize': FONTSIZE, # was FONTSIZE
'xtick.labelsize': FONTSIZE,
'ytick.labelsize': FONTSIZE,
'font.family': 'Bitstream Vera Sans'
}
rcParams.update(params)
def format_axes(ax, tick_size=4):
for axis in [ax.xaxis, ax.yaxis]:
axis.set_tick_params(direction='out', color='k', size=tick_size)
return ax
FIG_DIR = expanduser(
"~/Dropbox/MyWork/imperial/PhD/writing/papers/scientific-data-2014/figures"
)
BLUE = (0.2980392156862745, 0.4470588235294118, 0.6901960784313725)
GREEN = (0.3333333333333333, 0.6588235294117647, 0.40784313725490196)
},
inplace=True,
)
data.head()
# In[154]:
data.info()
# In[177]:
from pylab import rcParams
from statsmodels.tsa.seasonal import seasonal_decompose
rcParams.update({
"figure.figsize": (20, 16),
"font.size": 16,
})
idx = pd.Index(sm.tsa.datetools.dates_from_range("1749", "2017"))
result = seasonal_decompose(data.sunspots[idx], model="additive")
result.plot()
# In[190]:
from pandas.plotting import autocorrelation_plot
rcParams.update({"figure.figsize": (20, 8)})
autocorrelation_plot(data.sunspots)
def plot_seg(data_fh,
target,
xlabel,
ylabel,
lower,
mean,
upper,
title,
x_label,
y_label,
x_order,
y_order,
fig_width,
fig_height,
fontsize,
x_label_rotation='vertical',
no_legend=False,
linewidth=1,
dpi=300,
separator=False):
'''
xlabel: category
ylabel: sub-category
zlabel:
'''
logging.info('starting...')
import matplotlib.style
matplotlib.style.use('seaborn')
rcParams.update({'lines.markeredgewidth': 0.1}) # seaborn removes fliers
included = total = 0
results = collections.defaultdict(dict)
xvals = set()
yvals = set()
for row in csv.DictReader(data_fh, delimiter='\t'):
try:
included += 1
xval = row[xlabel] # group axis name
yval = row[ylabel] # sub-group axis name
results[xval][yval] = (float(row[mean]), float(row[lower]),
float(row[upper]))
xvals.add(xval)
yvals.add(yval)
except:
logging.warn('Failed to include %s', row)
total += 1
logging.info('finished reading %i of %i records', included, total)
logging.debug('xvals %s yvals %s results %s', xvals, yvals, results)
if len(results) == 0:
logging.warn('No data to plot')
return
logging.debug('xvals %s yvals %s', xvals, yvals)
#fig, ax = plt.subplots()
#fig_width = min(18, max(6, len(xvals) * len(yvals)))
#fig = plt.figure(figsize=(fig_width, fig_width * 0.7))
rcParams.update({'font.size': fontsize})
fig = plt.figure(figsize=(fig_width, fig_height))
plt.rc('legend', fontsize=fontsize)
ax = fig.add_subplot(111)
ax.tick_params(axis='x', labelsize=fontsize)
ax.tick_params(axis='y', labelsize=fontsize)
ax.grid(axis='y', linewidth=0) # no lines on y-axis
#ax.errorbar(list(xvals), , xerr=errs, fmt='o')
# do each sub-category with a different color
for ydx, yval in enumerate(sorted(yvals)):
xs = []
ys = []
ls = []
us = []
for xdx, xval in enumerate(sorted(xvals)):
position = xdx * len(yvals) + ydx
xs.append(position)
ys.append(results[xval][yval][0])
ls.append(results[xval][yval][0] - results[xval][yval][1])
us.append(results[xval][yval][2] - results[xval][yval][0])
logging.debug('xs %s ys %s for %i', xs, ys, ydx)
ax.errorbar(ys, xs, xerr=[ls, us], fmt='o', c=COLORS[ydx], label=yval)
if separator:
for x in range(len(xvals) - 1):
ax.axhline((x + 1) * len(yvals) - 0.5, color='white', linewidth=1)
labels = sorted(xvals)
positions = [
x * len(yvals) + ((len(yvals) - 1) / 2) for x in range(len(xvals))
]
ax.yaxis.set_major_locator(matplotlib.ticker.FixedLocator(positions))
ax.yaxis.set_major_formatter(matplotlib.ticker.FixedFormatter(labels))
ax.legend()
ax.set_title(title)
#if separator:
# for i, x in enumerate(ind[:-1]):
# ax.axvline((x + ind[i+1]) / 2, color='white', linewidth=1)
# logging.debug('vline at %f', x)
# Add some text for labels, title and custom x-axis tick labels, etc.
if y_label is not None:
ax.set_ylabel(y_label, fontsize=fontsize)
if x_label is not None:
ax.set_xlabel(x_label, fontsize=fontsize)
ax.set_title(title, fontsize=fontsize)
logging.info('done processing %i of %i. plot at dpi %i', included, total,
dpi)
plt.tight_layout()
plt.savefig(target, dpi=dpi)
matplotlib.pyplot.close('all')
import os
import argparse
import csv
import matplotlib.pyplot as plt
from pylab import rcParams
rcParams['pdf.fonttype'] = 42
rcParams['ps.fonttype'] = 42
rcParams.update({'font.size': 44})
BLOCKS_PER_CHUNK = 16
BLOCKS_PER_PLAYER_LOG_LENGTH_UNIT = 150
CAPTION_REPLACEMENTS = {
"1x1": "Single Server Setting",
"2x1": "Two Server Setting",
"3x1": "Three Server Setting",
"2x2": "Four Server Setting",
"3x3": "Nine Server Setting"
}
def readPlayerFiles(directory):
player_movement_info = {}
for file in os.listdir(directory):
if file.startswith("player_") and file.endswith("_movement.csv"):
id = file.split("_")[1]
movement = {"x": [], "z": []}
with open(os.path.join(directory, file), 'rt') as csvfile:
reader = csv.reader(csvfile, delimiter="\t")