def test_vaccines_sequential(do_plot=False):
sc.heading('Testing sequential vaccine...')
n_days = 60
p1 = cv.variant('beta', days=20, n_imports=20)
num_doses = {i:(i**2)*(i%2==0) for i in np.arange(n_days)} # Test subtarget and fluctuating doses
n_doses = []
subtarget = dict(inds=np.arange(int(base_pars.pop_size//2)), vals=0.1)
pfizer = cv.vaccinate_num(vaccine='pfizer', sequence='age', num_doses=num_doses, subtarget=subtarget)
sim = cv.Sim(base_pars, n_days=n_days, rescale=False, use_waning=True, variants=p1, interventions=pfizer, analyzers=lambda sim: n_doses.append(sim.people.doses.copy()))
sim.run()
n_doses = np.array(n_doses)
if do_plot:
fully_vaccinated = (n_doses == 2).sum(axis=1)
first_dose = (n_doses == 1).sum(axis=1)
pl.stackplot(sim.tvec, first_dose, fully_vaccinated)
# Stacked bars by 10 year age
# At the end of the simulation
df = pd.DataFrame(n_doses.T)
df['age_bin'] = np.digitize(sim.people.age,np.arange(0,100,10))
df['fully_vaccinated'] = df[60]==2
df['first_dose'] = df[60]==1
df['unvaccinated'] = df[60]==0
out = df.groupby('age_bin').sum()
out[["unvaccinated", "first_dose","fully_vaccinated"]].plot(kind="bar", stacked=True)
# Part-way through the simulation
df = pd.DataFrame(n_doses.T)
df['age_bin'] = np.digitize(sim.people.age,np.arange(0,100,10))
df['fully_vaccinated'] = df[40]==2
df['first_dose'] = df[40]==1
df['unvaccinated'] = df[40]==0
out = df.groupby('age_bin').sum()
out[["unvaccinated", "first_dose","fully_vaccinated"]].plot(kind="bar", stacked=True)
return sim
def test_vaccines_sequential(do_plot=False):
sc.heading('Testing sequential vaccine...')
p1 = cv.variant('sa variant', days=20, n_imports=20)
def age_sequence(people): return np.argsort(-people.age)
n_doses = []
pfizer = cv.vaccinate_num(vaccine='pfizer', sequence=age_sequence, num_doses=lambda sim: sim.t)
sim = cv.Sim(base_pars, rescale=False, use_waning=True, variants=p1, interventions=pfizer, analyzers=lambda sim: n_doses.append(sim.people.vaccinations.copy()))
sim.run()
n_doses = np.array(n_doses)
if do_plot:
fully_vaccinated = (n_doses == 2).sum(axis=1)
first_dose = (n_doses == 1).sum(axis=1)
pl.stackplot(sim.tvec, first_dose, fully_vaccinated)
# Stacked bars by 10 year age
# At the end of the simulation
df = pd.DataFrame(n_doses.T)
df['age_bin'] = np.digitize(sim.people.age,np.arange(0,100,10))
df['fully_vaccinated'] = df[60]==2
df['first_dose'] = df[60]==1
df['unvaccinated'] = df[60]==0
out = df.groupby('age_bin').sum()
out[["unvaccinated", "first_dose","fully_vaccinated"]].plot(kind="bar", stacked=True)
# Part-way through the simulation
df = pd.DataFrame(n_doses.T)
df['age_bin'] = np.digitize(sim.people.age,np.arange(0,100,10))
df['fully_vaccinated'] = df[40]==2
df['first_dose'] = df[40]==1
df['unvaccinated'] = df[40]==0
out = df.groupby('age_bin').sum()
out[["unvaccinated", "first_dose","fully_vaccinated"]].plot(kind="bar", stacked=True)
return sim
def plot_tickets(tracker_name, db_name, image_name):
conn = sqlite3.connect(db_name)
cur = conn.cursor()
rows = cur.execute("select json from tickets")
all_issues = (json.loads(r[0]) for r in rows)
issues = [i for i in all_issues if i["tracker"]["name"] == tracker_name]
cur.close()
conn.close()
data = [reduce_journal(analyse_journal(i)) for i in issues]
data = itertools.chain.from_iterable(data)
data = sorted(data, key=lambda x: x[0])
data = [split_sum(d) for d in sum_journal(data)]
pylab.figure(figsize=(9, 10))
pylab.subplot(2, 1, 1)
pylab.title(tracker_name)
x1, y1 = zip(*data)
y1 = zip(*y1)
pylab.grid()
pylab.stackplot(x1, y1, colors=COLORS)
high = pylab.Rectangle((0, 0), 1, 1, fc=COLORS[0])
normal = pylab.Rectangle((0, 0), 1, 1, fc=COLORS[1])
low = pylab.Rectangle((0, 0), 1, 1, fc=COLORS[2])
pylab.legend([low, normal, high], ["Low", "Normal", "High"], loc=2)
pylab.subplot(2, 1, 2)
pylab.title(tracker_name + " (Die letzen 5 Wochen)")
current_data = filter_by_time(data, datetime.timedelta(days=45))
x2, y2 = zip(*current_data)
y2 = zip(*y2)
pylab.grid()
pylab.stackplot(x2, y2, colors=COLORS)
pylab.savefig(image_name)
# Handle MOOSE developers option
if options.moose_dev and options.authors:
raise Exception("Can not specify both --authors and --moose-dev");
elif options.moose_dev:
options.authors = 'moose'
# Extract the data
dates, data, contrib, contributors = getData(options)
# Create the figure
fig, ax1 = pylab.subplots()
# Plot the data
if options.stack: # stack plot
handles = pylab.stackplot(dates, data)
for i in range(len(handles)):
handles[i].set_label(contributors[i])
else: # line plot
handles = []
for i in range(len(contributors)):
x = numpy.array(dates)
y = data[i,:]
idx = y>0
h = ax1.plot(x[idx], y[idx], label=contributors[i], linewidth=2, markevery=60, marker=marker.next(), color=color.next())
handles.append(h[0])
lgnd = pylab.legend(handles, contributors, loc='upper left')
lgnd.draw_frame(False)
# Add labels
def MakePlot(infile, plotfile, aa_order):
"""Makes plots from input file *infile*.
*infile* contains the amino-acid frequencies over time.
*plotfile* is the name of the created PDF.
*aa_order* is a list giving the order of amino acids from top to bottom.
"""
labelfrac = 0.01 # only label residues that rise above this level in legend
# read input file
aminoacids = mapmuts.sequtils.AminoAcids()
colors = ['b', 'r', 'g', 'c', 'm', 'y'
] + ['k'] * len(aminoacids) # order corresponds to aa_order
colors = colors[:len(aminoacids)]
colors.reverse()
aa_order.reverse()
aa_indices = dict([(aa_order[i], i) for i in range(len(aa_order))])
lines = [
line for line in open(infile).readlines()
if not (line.isspace() or line[0] == '#')
]
dates = []
counts = []
aafracs = [[] for aa in aminoacids]
for line in lines:
entries = line.split('\t')
dates.append(float(entries[0]))
counts.append(int(entries[3]))
i = 0
for aa in aminoacids:
frac = float(entries[4 + i]) / counts[-1]
aafracs[aa_indices[aa]].append(frac)
i += 1
labeled_aas = []
for i in range(len(aa_order)):
aa = aa_order[i]
maxfrac = max(aafracs[i])
if maxfrac >= labelfrac:
labeled_aas.append(aa)
# make plot
matplotlib.rc('font', size=9)
fig = pylab.figure(figsize=(3, 1.9))
(lmargin, bmargin, rmargin, tmargin) = (0.15, 0.22, 0.01, 0.18)
axis = pylab.axes(
[lmargin, bmargin, 1.0 - lmargin - rmargin, 1.0 - tmargin - bmargin])
plotareas = pylab.stackplot(dates, aafracs, colors=colors)
pylab.ylabel('fraction', size=10)
pylab.xlabel('date', size=10)
axis.set_ylim([0, 1.0])
yticker = matplotlib.ticker.FixedLocator([0.0, 0.5, 1.0])
axis.yaxis.set_major_locator(yticker)
axis.set_xlim([min(dates), max(dates)])
dateticks = [
year for year in range(int(math.ceil(min(dates))),
int(max(dates)) + 1)
]
xticker = matplotlib.ticker.FixedLocator(dateticks)
axis.xaxis.set_major_locator(xticker)
yformatter = matplotlib.ticker.FixedFormatter(
['%d' % year for year in dateticks])
axis.xaxis.set_major_formatter(yformatter)
patches = []
i = 0
assert len(aa_order) == len(plotareas) == len(colors)
for (aa, color) in zip(aa_order, colors):
if aa in labeled_aas:
patch = pylab.Rectangle((0, 0), 1, 1, color=color)
patches.append(patch)
i += 1
assert len(labeled_aas) == len(patches)
patches.reverse()
labeled_aas.reverse()
pylab.legend(patches,
labeled_aas,
ncol=len(labeled_aas),
handlelength=0.8,
fontsize=10,
handletextpad=0.4,
columnspacing=1,
bbox_to_anchor=(0.5, 0.98),
loc='lower center')
print "Creating the plot file %s" % plotfile
pylab.savefig(plotfile)
print "Now making JPG version."
jpgfile = "%s.jpg" % os.path.splitext(plotfile)[0]
os.system('convert -density 500 %s %s' % (plotfile, jpgfile))
b=cnm.estimates.b,
f=cnm.estimates.f,
dims=cnm.estimates.dims,
tottime='SEE VARIABLES t_XX',
noisyC=cnm.estimates.noisyC,
shifts=cnm.estimates.shifts,
num_comps=cnm.estimates.A.shape[-1],
t_online=cnm.t_online,
t_detect=cnm.t_detect,
t_shapes=cnm.t_shapes)
cnm.save(
os.path.join(
base_folder,
'Zebrafish/results_analysis_online_1EPOCH_gSig6_equalized_Plane_NEW_'
+ str(ID) + '.hdf5'))
#%%
pl.figure()
pl.stackplot(
np.arange(len(cnm.t_detect)), 1e3 * np.array(cnm.t_motion),
1e3 * (np.array(cnm.t_online) - np.array(cnm.t_detect) -
np.array(cnm.t_shapes) - np.array(cnm.t_motion)),
1e3 * np.array(cnm.t_detect), 1e3 * np.array(cnm.t_shapes))
pl.title('Processing time per frame')
pl.xlabel('Frame #')
pl.ylabel('Processing time [ms]')
pl.ylim([0, 1000])
pl.legend(labels=['motion', 'process', 'detect', 'shapes'])
#%%
cnm.estimates.plot_contours()
utils = pad(utils)
labels = ['ADAM',
'Avocado',
'Cannoli',
'BDG-Formats',
'Utils']
figure()
title('Lines of code across the BDG projects over time')
xlabel('Time')
ylabel('Lines of source code')
stackplot(dates,
adam_core, adam_apis, adam_cli, adam_python, adam_r,
avocado_core, avocado_cli, cannoli, formats, utils,
colors = ["#FF0000", "#EE0000", "#DD0000", "#CC0000", "#BB0000",
"#00FF00", "#00DD00",
"#0000FF",
"#FF00FF",
"#808080"], edgecolor='none')
legend([Patch(color="#FF0000"),
Patch(color="#00FF00"),
Patch(color="#0000FF"),
Patch(color="#FF00FF"),
Patch(color="#808080")],
labels,
loc=2)
xlim(0, (last_date - start_date).days)
xticks([0,
(date(2014, 1, 1) - start_date).days,
(date(2015, 1, 1) - start_date).days,
(date(2016, 1, 1) - start_date).days,
def MakePlot(infile, plotfile, aa_order):
"""Makes plots from input file *infile*.
*infile* contains the amino-acid frequencies over time.
*plotfile* is the name of the created PDF.
*aa_order* is a list giving the order of amino acids from top to bottom.
"""
labelfrac = 0.01 # only label residues that rise above this level in legend
# read input file
aminoacids = mapmuts.sequtils.AminoAcids()
colors = ['b', 'r', 'g', 'c', 'm', 'y'] + ['k'] * len(aminoacids) # order corresponds to aa_order
colors = colors[ : len(aminoacids)]
colors.reverse()
aa_order.reverse()
aa_indices = dict([(aa_order[i], i) for i in range(len(aa_order))])
lines = [line for line in open(infile).readlines() if not (line.isspace() or line[0] == '#')]
dates = []
counts = []
aafracs = [[] for aa in aminoacids]
for line in lines:
entries = line.split('\t')
dates.append(float(entries[0]))
counts.append(int(entries[3]))
i = 0
for aa in aminoacids:
frac = float(entries[4 + i]) / counts[-1]
aafracs[aa_indices[aa]].append(frac)
i += 1
labeled_aas = []
for i in range(len(aa_order)):
aa = aa_order[i]
maxfrac = max(aafracs[i])
if maxfrac >= labelfrac:
labeled_aas.append(aa)
# make plot
matplotlib.rc('font', size=9)
fig = pylab.figure(figsize=(3, 1.9))
(lmargin, bmargin, rmargin, tmargin) = (0.15, 0.22, 0.01, 0.18)
axis = pylab.axes([lmargin, bmargin, 1.0 - lmargin - rmargin, 1.0 - tmargin - bmargin])
plotareas = pylab.stackplot(dates, aafracs, colors=colors)
pylab.ylabel('fraction', size=10)
pylab.xlabel('date', size=10)
axis.set_ylim([0, 1.0])
yticker = matplotlib.ticker.FixedLocator([0.0, 0.5, 1.0])
axis.yaxis.set_major_locator(yticker)
axis.set_xlim([min(dates), max(dates)])
dateticks = [year for year in range(int(math.ceil(min(dates))), int(max(dates)) + 1)]
xticker = matplotlib.ticker.FixedLocator(dateticks)
axis.xaxis.set_major_locator(xticker)
yformatter = matplotlib.ticker.FixedFormatter(['%d' % year for year in dateticks])
axis.xaxis.set_major_formatter(yformatter)
patches = []
i = 0
assert len(aa_order) == len(plotareas) == len(colors)
for (aa, color) in zip(aa_order, colors):
if aa in labeled_aas:
patch = pylab.Rectangle((0, 0), 1, 1, color=color)
patches.append(patch)
i += 1
assert len(labeled_aas) == len(patches)
patches.reverse()
labeled_aas.reverse()
pylab.legend(patches, labeled_aas, ncol=len(labeled_aas), handlelength=0.8, fontsize=10, handletextpad=0.4, columnspacing=1, bbox_to_anchor=(0.5, 0.98), loc='lower center')
print "Creating the plot file %s" % plotfile
pylab.savefig(plotfile)
print "Now making JPG version."
jpgfile = "%s.jpg" % os.path.splitext(plotfile)[0]
os.system('convert -density 500 %s %s' % (plotfile, jpgfile))
beta=0.015,
n_days=90,
)
# Define the prioritization function
def prioritize_by_age(people):
return np.argsort(-people.age)
# Record the number of doses each person has received each day so
# that we can plot the rollout in this example. Without a custom
# analyzer, only the total number of doses will be recorded
n_doses = []
# Define sequence based vaccination
pfizer = cv.vaccinate_num(vaccine='pfizer',
sequence=prioritize_by_age,
num_doses=100)
sim = cv.Sim(pars=pars,
interventions=pfizer,
analyzers=lambda sim: n_doses.append(sim.people.doses.copy()))
sim.run()
pl.figure()
n_doses = np.array(n_doses)
fully_vaccinated = (n_doses == 2).sum(axis=1)
first_dose = (n_doses == 1).sum(axis=1)
pl.stackplot(sim.tvec, first_dose, fully_vaccinated)
pl.legend(['First dose', 'Fully vaccinated'])
pl.show()
if code=='2':
quotas[1].append(cuml)
if code=='3':
quotas[2].append(cuml)
if code=='4':
quotas[3].append(cuml)
if code=='6':
quotas[4].append(cuml)
if code=='7':
quotas[5].append(cuml)
years = range(1989,2013)
print quotas
plot = plt.stackplot(years,quotas[0],quotas[1],quotas[2],quotas[3],quotas[4],quotas[5],
colors=['#377EB8','#7E1137','y','r','m','#55BA87'])
plt.legend(["East Asia & Pacific","Europe & Central Asia","Latin America","Middle East & N Africa","South Asia","Sub-Saharan Africa"], loc='upper left')
plt.xlim(1989,2012)
plt.ylabel('Total Number of Countries with a Gender Quota')
plt.xlabel('Year')
plt.savefig('quotas/quotasStacked.png')
plt.savefig('quotas/quotasStacked.eps',bbox_inches='tight')
plt.gcf().clear()
#Reserved Only
data = open('quotaTimesRes.csv','r')
quotas = [[],[],[],[],[],[]]
formats.append(formats[-1])
utils.append(utils[-1])
labels = ['ADAM',
'Avocado',
'Cannoli',
'BDG-Formats',
'Utils']
figure()
title('Unique contributors across the BDG projects over time')
xlabel('Time')
ylabel('Unique contributors')
stackplot(dates, adam, avocado, cannoli, formats, utils,
colors = ["#FF0000",
"#00FF00",
"#0000FF",
"#FF00FF",
"#808080"])
legend([Patch(color="#FF0000"),
Patch(color="#00FF00"),
Patch(color="#0000FF"),
Patch(color="#FF00FF"),
Patch(color="#808080")],
['ADAM', 'Avocado', 'Cannoli', 'bdg-formats', 'bdg-utils'],
loc=2)
xlim(0, (last_date - start_date).days)
xticks([0,
(date(2014, 1, 1) - start_date).days,
(date(2015, 1, 1) - start_date).days,
(date(2016, 1, 1) - start_date).days,
(date(2017, 1, 1) - start_date).days,
try:
with h5py.File(fname, "r") as h5:
Hp = h5[table][:]
except KeyError, e:
logger.info("Failed to read data from %s: %s" % (fname, e))
exit(1)
except IOError, e:
logger.info("Failed to open %s fname: %s" % (fname, e))
exit(1)
epochs = Hp.shape[0]
n_layers = Hp.shape[1]
if args.stacked:
ylim = 2 * Hp[-1].sum()
pylab.ylim([ylim, 0])
pylab.stackplot(np.arange(epochs), Hp[:, ::-1].T)
else:
ylim = 2 * Hp[-1].min()
pylab.ylim([ylim, 0])
pylab.plot(Hp)
#pylab.figsize(12, 8)
pylab.xlabel("Epochs")
#pylab.ylabel("avg_{x~testdata} log( E_{h~q}[p(x,h)/q(h|x)]")
pylab.legend(["layer %d" % i for i in xrange(n_layers)], loc="lower right")
pylab.show(block=True)
import numpy as np import pylab as plt Y = [[3,4,5,6],[2,2,2,2],[1,2,3,1]] X = range(4) plt.stackplot(X, Y, baseline="zero") plt.show()