def plotAllData( self, allData, particleSize, room ):
dates = allData['Time']
print 'Plotting and saving averages'
for shift in ['Day Shift','Night Shift','Empty Lab']:
pyplot.figure(num=None, figsize=(16,9), dpi=100, facecolor='w', edgecolor='k')
pyplot.plot_date(dates,allData[shift + ' Avg'], 'b-')
pyplot.xlabel( 'Date (MT) ')
pyplot.ylabel( 'Average Count' )
pyplot.title( room + ' ' + shift + ' Averages for ' + particleSize + ' um Counts' )
pyplot.savefig( room + particleSize + 'Avg' + shift[:-6] + '.png')
pyplot.set_yscale('log')
pyplot.title( room + ' ' + shift + ' Averages for ' + particleSize + ' um Counts Log Scale' )
pyplot.savefig( room + particleSize + 'Avg' + shift[:-6] + '.png')
print 'Plotting and saving baselines'
for shift in ['Day Shift','Night Shift','Empty Lab']:
pyplot.figure(num=None, figsize=(16,9), dpi=100, facecolor='w', edgecolor='k')
pyplot.plot_date(dates,allData[shift + ' Base'], 'b-')
pyplot.xlabel( 'Date (MT)' )
pyplot.ylabel( 'Baseline' )
pyplot.title( room + ' ' + shift + ' Baselines for ' + particleSize + ' um Counts' )
pyplot.savefig( room + particleSize + 'Base' + shift[:-6] + '.png')
pyplot.set_yscale('log')
pyplot.title( room + ' ' + shift + ' Baselines for ' + particleSize + ' um Counts Log Scale' )
pyplot.savefig( room + particleSize + 'Base' + shift[:-6] + '.png')
def save_solutions(deps, s, trun_indx):
np.savetxt("solution.dat", deps)
np.savetxt("singular_values.dat", s)
np.savetxt("truncation_index.dat", trun_indx)
plt.plot(np.arange(np.shape(s)[0]), s, markerstyle="o", linewidth=2)
plt.title("Singular Values", fontsize=20)
plt.set_yscale("log")
plt.xlabel("index", fontsize=20)
plt.ylabel("Value", fontsize=20)
def save_solutions(deps, s, trun_indx):
np.savetxt("solution.dat", deps)
np.savetxt("singular_values.dat", s)
np.savetxt("truncation_index.dat", trun_indx)
plt.plot(np.arange(np.shape(s)[0]), s, markerstyle="o", linewidth=2)
plt.title("Singular Values", fontsize=20)
plt.set_yscale("log")
plt.xlabel("index", fontsize=20)
plt.ylabel("Value", fontsize=20)
def plot_metrics(self, plt, logarithmic, differences=False):
metrics = list(zip(*self.metric_history))[1:]
if len(metrics[0]) > 3:
for m in metrics[1:-2]:
if differences:
plt.plot(self.time_history[1:], list(np.diff(m)))
plt.set_ylabel('Daily change')
else:
plt.plot(self.time_history, m)
plt.set_ylabel('Cumulative')
if logarithmic:
plt.set_yscale('log')
labels = list(STATE_DESCRIPTIONS.values())[1:-2]
if differences:
labels = ['net newly ' + lb for lb in labels]
plt.legend(labels)
plt.set_xlabel('Days since first ' + str(self.params['geometry']['i']) + ' infections.')
def plot_many_curves(data, parameters=None, fname=None):
"""
Plot many curve on the given axis with the given parameters
"""
params = process_parameters(parameters)
plt.rc('font', **params['font'])
fig = plt.figure(num=None, figsize=params['size'], dpi=params['dpi'])
fig.clf()
axis = plt.subplot(111)
for d in data:
plt.plot(d[0], d[1], lw=1, label=d[2])
plt.grid(axis='y', color="0.9", linestyle='-', linewidth=1)
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.spines['left'].set_visible(False)
axis.get_xaxis().tick_bottom()
axis.get_yaxis().tick_left()
axis.tick_params(axis='x', direction='out')
axis.tick_params(axis='y', length=0)
for spine in axis.spines.values():
spine.set_position(('outward', 5))
axis.set_axisbelow(True)
axis.set_xlabel(params['xlabel'])
axis.set_ylabel(params['ylabel'])
if params['logscale'] == 'y':
plt.set_yscale('log')
if params['logscale'] == 'x':
plt.set_xscale('log')
plt.legend(loc='upper right', frameon=False)
# draw & show / save image
if fname is None:
plt.draw()
plt.show()
else:
plt.savefig(fname)
def graphDataDictionary( self, dataDictionary, particleSize, room ):
dayShiftDates = dataDictionary['Day Shift']['Time']
dayShiftCounts = dataDictionary['Day Shift']['Count']
nightShiftDates = dataDictionary['Night Shift']['Time']
nightShiftCounts = dataDictionary['Night Shift']['Count']
emptyLabDates = dataDictionary['Empty Lab']['Time']
emptyLabCounts = dataDictionary['Empty Lab']['Count']
pyplot.figure(num=None, figsize=(16,9), dpi=100, facecolor='w', edgecolor='k')
pyplot.plot_date(date2num(dayShiftDates),dayShiftCounts, 'r-')
pyplot.plot_date(date2num(nightShiftDates),nightShiftCounts, 'g-')
pyplot.plot_date(date2num(emptyLabDates),emptyLabCounts, 'b-')
pyplot.xlabel( 'Date/Time (MT)' )
pyplot.ylabel( 'Count' )
pyplot.title( room + ' ' + particleSize + ' um Counts' )
pyplot.savefig( room + particleSize + 'Plot.png' )
pyplot.set_yscale('log')
pyplot.title( room + ' ' + particleSize + ' um Counts Log Scale' )
pyplot.savefig( room + particleSize + 'PlotLog.png' )
def plotAllData(self, allData, particleSize, room):
dates = allData['Time']
print 'Plotting and saving averages'
for shift in ['Day Shift', 'Night Shift', 'Empty Lab']:
pyplot.figure(num=None,
figsize=(16, 9),
dpi=100,
facecolor='w',
edgecolor='k')
pyplot.plot_date(dates, allData[shift + ' Avg'], 'b-')
pyplot.xlabel('Date (MT) ')
pyplot.ylabel('Average Count')
pyplot.title(room + ' ' + shift + ' Averages for ' + particleSize +
' um Counts')
pyplot.savefig(room + particleSize + 'Avg' + shift[:-6] + '.png')
pyplot.set_yscale('log')
pyplot.title(room + ' ' + shift + ' Averages for ' + particleSize +
' um Counts Log Scale')
pyplot.savefig(room + particleSize + 'Avg' + shift[:-6] + '.png')
print 'Plotting and saving baselines'
for shift in ['Day Shift', 'Night Shift', 'Empty Lab']:
pyplot.figure(num=None,
figsize=(16, 9),
dpi=100,
facecolor='w',
edgecolor='k')
pyplot.plot_date(dates, allData[shift + ' Base'], 'b-')
pyplot.xlabel('Date (MT)')
pyplot.ylabel('Baseline')
pyplot.title(room + ' ' + shift + ' Baselines for ' +
particleSize + ' um Counts')
pyplot.savefig(room + particleSize + 'Base' + shift[:-6] + '.png')
pyplot.set_yscale('log')
pyplot.title(room + ' ' + shift + ' Baselines for ' +
particleSize + ' um Counts Log Scale')
pyplot.savefig(room + particleSize + 'Base' + shift[:-6] + '.png')
def graphDataDictionary(self, dataDictionary, particleSize, room):
dayShiftDates = dataDictionary['Day Shift']['Time']
dayShiftCounts = dataDictionary['Day Shift']['Count']
nightShiftDates = dataDictionary['Night Shift']['Time']
nightShiftCounts = dataDictionary['Night Shift']['Count']
emptyLabDates = dataDictionary['Empty Lab']['Time']
emptyLabCounts = dataDictionary['Empty Lab']['Count']
pyplot.figure(num=None,
figsize=(16, 9),
dpi=100,
facecolor='w',
edgecolor='k')
pyplot.plot_date(date2num(dayShiftDates), dayShiftCounts, 'r-')
pyplot.plot_date(date2num(nightShiftDates), nightShiftCounts, 'g-')
pyplot.plot_date(date2num(emptyLabDates), emptyLabCounts, 'b-')
pyplot.xlabel('Date/Time (MT)')
pyplot.ylabel('Count')
pyplot.title(room + ' ' + particleSize + ' um Counts')
pyplot.savefig(room + particleSize + 'Plot.png')
pyplot.set_yscale('log')
pyplot.title(room + ' ' + particleSize + ' um Counts Log Scale')
pyplot.savefig(room + particleSize + 'PlotLog.png')
grp3, bin3 = np.histogram(h3, bins=range(0, max(h3) + 1), density=False)
grp4, bin4 = np.histogram(h4, bins=range(0, max(h4) + 1), density=False)
hist1, bin1 = np.histogram(h1, bins=range(0, max(h1) + 1), density=True)
hist2, bin2 = np.histogram(h2, bins=range(0, max(h2) + 1), density=True)
hist3, bin3 = np.histogram(h3, bins=range(0, max(h3) + 1), density=True)
hist4, bin4 = np.histogram(h4, bins=range(0, max(h4) + 1), density=True)
# # PDF plot in log-log scale
# l1, = plt.loglog(range(1, max(h1)+1), hist1, '-o')
# l2, = plt.loglog(range(1, max(h2)+1), hist2, '-o')
# l3, = plt.loglog(range(1, max(h3)+1), hist3, '-o')
# l4, = plt.loglog(range(1, max(h4)+1), hist4, '-o')
cs1 = np.cumsum(grp1) / float(len(h1))
cs2 = np.cumsum(grp2) / float(len(h2))
cs3 = np.cumsum(grp3) / float(len(h3))
cs4 = np.cumsum(grp4) / float(len(h4))
# ECDF plot
l1, = plt.plot(range(0, max(h1)), cs1, '-o', sety)
l2, = plt.plot(range(0, max(h2)), cs2, '--')
l3, = plt.plot(range(0, max(h3)), cs3, '-')
l4, = plt.plot(range(0, max(h4)), cs4)
plt.legend((l1, l2, l3, l4), ('GOOD', 'VF', 'SH', 'ALL'), loc='lower right')
plt.xlim(0, 3000)
plt.set_yscale('log')
# plt.ylim(0, 0.002)
plt.show()
def density_profile(sim, linestyle=False, center=True, clear=True, fit=False,in_units=None,
filename=None, fit_factor=0.02, axes=False, **kwargs):
'''
3d density profile
**Options:**
*filename* (None): name of file to which to save output
**Usage:**
>>> import pynbody.plot as pp
>>> h = s.halos()
>>> pp.density_profile(h[1],linestyle='dashed',color='k')
'''
if axes: plt = axes
else: import matplotlib.pyplot as plt
global config
logger.info("Centering...")
if center:
halo.center(sim, mode='ssc')
logger.info("Creating profile...")
if 'min' in kwargs:
ps = profile.Profile(
sim, ndim=3, type='log', nbins=40, min=kwargs['min'])
del kwargs['min']
else:
ps = profile.Profile(sim, ndim=3, type='log', nbins=40)
if clear and not axes:
plt.clf()
critden = (units.Unit('100 km s^-1 Mpc^-1')
* sim.properties['h']) ** 2 / 8.0 / np.pi / units.G
r = ps['rbins'].in_units('kpc')
if in_units is None:
den = ps['density'].in_units(critden)
else:
den = ps['density'].in_units(in_units)
if linestyle:
plt.errorbar(r, den, yerr=den / np.sqrt(ps['n']),
linestyle=linestyle, **kwargs)
else:
plt.errorbar(r, den, yerr=den / np.sqrt(ps['n']),
fmt='o', **kwargs)
if in_units is None:
ylabel=r'$\rho / \rho_{cr}$' # +den.units.latex()+'$]')
else:
ylabel=r'$\rho / '+den.units.latex()+'$'
if axes:
plt.set_yscale('log')
plt.set_xscale('log')
plt.set_xlabel('r [kpc]')
plt.set_ylabel(ylabel) #r'$\rho / \rho_{cr}$') # +den.units.latex()+'$]')
else:
plt.yscale('log')
plt.xscale('log')
plt.xlabel('r [kpc]')
plt.ylabel(ylabel) #r'$\rho / \rho_{cr}$') # +den.units.latex()+'$]')
if (filename):
logger.info("Saving %s", filename)
plt.savefig(filename)
if fit:
fit_inds = np.where(r < fit_factor*sim['r'].max())
alphfit = np.polyfit(np.log10(r[fit_inds]),
np.log10(den[fit_inds]), 1)
# print "alpha: ", alphfit[0], " norm:", alphfit[1]
fit = np.poly1d(alphfit)
plt.plot(r[fit_inds], 10**fit(np.log10(r[fit_inds])),
color='k',linestyle='dashed',
label=r'$\alpha$=%.1f'%alphfit[0])
plt.legend(loc=3)
return alphfit[0]
def plot_shaded_plot(x, y, parameters=None, fname=None):
"""
Plot boxplot for set of data (median and shaded quartiles)
where data is a list of lists of values
data = [ [...] , ... , [...] ]
"""
params = process_parameters(parameters)
plt.rc('font', **params['font'])
perc_05, perc_25, med, perc_75, perc_95 = percentiles(y)
fig = plt.figure(num=None, figsize=params['size'], dpi=params['dpi'])
axis = plt.subplot(111)
plt.clf()
if params['quartiles']:
plt.fill_between(x,
perc_25,
perc_75,
alpha=0.5,
linewidth=0.1,
color=params['quart-color'])
plt.fill_between(x,
perc_75,
perc_95,
alpha=0.25,
linewidth=0.1,
color=params['quart-color'])
plt.fill_between(x,
perc_05,
perc_25,
alpha=0.25,
linewidth=0.1,
color=params['quart-color'])
plt.plot(x,
med,
lw=1,
alpha=1.0,
label=params['label'],
color=params['quart-color'])
plt.grid(axis='y', color="0.9", linestyle='-', linewidth=1)
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.spines['left'].set_visible(False)
axis.get_xaxis().tick_bottom()
axis.get_yaxis().tick_left()
axis.tick_params(axis='x', direction='out')
axis.tick_params(axis='y', length=0)
for spine in axis.spines.values():
spine.set_position(('outward', 5))
axis.set_axisbelow(True)
axis.set_xlabel(params['xlabel'])
axis.set_ylabel(params['ylabel'])
if params['logscale'] == 'y':
plt.set_yscale('log')
if params['logscale'] == 'x':
plt.set_xscale('log')
# draw & show / save image
if fname is None:
plt.draw()
plt.show()
else:
plt.savefig(fname)
fig = 0
for h in histograms:
fig += 1
if plotallhistos or h.title.find(opts.SINGLETITLE)>-1:
centers = []
widths = []
sumw = []
sumw2 = []
for bin in h:
centers.append(bin.midpoint)
widths.append(bin.width)
sumw.append(bin.sumW)
sumw2.append(bin.sumW2)
yerr = map(lambda x: math.sqrt(x), sumw2)
if opts.LOGY:
plt.set_yscale("log")
if opts.LOGX:
plt.set_xscale("log")
thislegend = Legend(h.title,h.title,"green")
if opts.LEGEND!="":
leg = readLegend(opts.LEGEND)
for l in leg:
if l.title==h.title:
thislegend=l
#ax.errorbar(centers, sumw, yerr=yerr, xerr=widths, fmt='o')
plt.figure(fig)
plt.hist(centers,bins=len(centers),weights=sumw,normed=opts.NORM,facecolor=thislegend.color,alpha=0.5)
plt.title(thislegend.nicename)
plt.show()
# title += "capacity = " + str(config.capacity) + ", n = " + str(config.n_atoms)
# title += "<br>w<sub>c</sub> = " + wc_str(config.wc)
# title += "<br>w<sub>a</sub> = " + \
# "[" + ", ".join([wa_str(i) for i in config.wa]) + "]"
# title += "<br>g = " + "[" + ", ".join([g_str(i) for i in config.g]) + "]"
# title += "<br>t = " + T_str(config.T)
# title += "<br>l = " + wc_str(config.l)
title += "</b>"
plt.set_title(title)
plt.set_xaxis("states")
plt.set_yaxis("time, " + T_str_mark(config.T))
plt.set_zaxis("prob.")
plt.set_yscale(y_scale)
plt.set_width(900)
plt.set_height(650)
plt.plot(
x_csv=config.x_csv,
y_csv=config.path + "/" + "t.csv",
z_csv=config.path + "/" + "z.csv",
# t_coeff=20000 / 1000 * (config.T / 1e-6),
online=False,
path=config.path,
filename="BipartiteGeneralLindblad",
)
# ---------------------------------------------------------------------------------------------------------------------
def plot(title,
label,
train_results,
test_results,
ax=None,
yscale='linear',
save_path=None,
extra_pt=None,
extra_pt_label=None):
"""Plot learning curves.
Args:
title (str): Title of plot
label (str): x-axis label
train_results (list): Results vector of training of length of number
of epochs trained. Could be loss or accuracy.
val_results (list): Results vector of validation of length of number
of epochs. Could be loss or accuracy.
yscale (str, optional): Matplotlib.pyplot.yscale parameter.
Defaults to 'linear'.
save_path (str, optional): If passed, figure will be saved at this path.
Defaults to None.
extra_pt (tuple, optional): Tuple of length 2, defining x and y coordinate
of where an additional black dot will be plotted. Defaults to None.
extra_pt_label (str, optional): Legend label of extra point. Defaults to None.
"""
epoch_array = np.arange(len(train_results)) + 1
train_label, test_label = "Training " + label.lower(
), "Test " + label.lower()
sns.set(style='ticks')
#if ax is not None:
# plt = ax
plt.plot(epoch_array,
train_results,
epoch_array,
test_results,
linestyle='dashed',
marker='o')
legend = ['Train results', 'Test results']
if extra_pt:
####################
## YOUR CODE HERE ##
####################
plt.scatter(extra_pt[0] + 1, extra_pt[1], c='black', zorder=9999)
# END OF YOUR CODE #
plt.legend(legend)
if ax is not None:
plt.set_xlabel('Epoch')
plt.set_ylabel(label)
plt.set_yscale(yscale)
sns.despine(trim=True, offset=5)
plt.set_title(title, fontsize=15)
else:
plt.xlabel('Epoch')
plt.ylabel(label)
plt.yscale(yscale)
sns.despine(trim=True, offset=5)
plt.title(title, fontsize=15)
if save_path:
plt.savefig(str(save_path), bbox_inches='tight')
if ax is None:
plt.show()