def test_pcolormesh_lognorm():
fig, ax = plt.subplots(1)
np.random.seed(10)
x = np.abs(np.random.randn(10, 10))
ppl.pcolormesh(fig, ax, x, norm=LogNorm(vmin=x.min().min(), vmax=x.max().max()))
def add_content(subcluster, content, suffix):
fig, ax = plt.subplots(1, figsize=(6.5,2.5))
for childax in ax.get_children():
if isinstance(childax, mpl.spines.Spine):
childax.set_color('#aaaaaa')
for i in ax.get_xticklabels():
i.set_color('#aaaaaa')
for i in ax.get_yticklabels():
i.set_color('#aaaaaa')
subcluster = sorted(subcluster, key=lambda t: max(t[1:].astype(float)), reverse=True)[:10]
subcluster = np.array(subcluster)
words = subcluster[:,0]
ys = subcluster[:,1:].astype(float)
mean = [np.mean(ys[:,i]) for i in xrange(ys.shape[1])]
ys = ys.transpose()
ax.set_ylim(top=max(10, max(map(max, ys))))
ppl.plot(ax, xs, ys, alpha=0.3, color="#7777ee")
ppl.plot(ax, xs, mean, alpha=1, color="black")
fname = './plots/%s_%s.png' % (conf, suffix)
fig.savefig(fname, format='png')
maxes = map(max, ys)
idx = maxes.index(max(maxes))
content.append(('', words, fname, idx))
def test_pcolormesh_negative():
fig, ax = plt.subplots(1)
np.random.seed(10)
ppl.pcolormesh(fig, ax, -np.random.uniform(size=(10, 10)),
xticklabels=UPPERCASE_CHARS[:10],
yticklabels=LOWERCASE_CHARS[-10:])
def test_pcolormesh_other_cmap():
purple_green = brewer2mpl.get_map('PRGn', 'diverging', 11).mpl_colormap
fig, ax = plt.subplots(1)
np.random.seed(10)
ppl.pcolormesh(fig, ax, np.random.randn(10, 10), cmap=purple_green)
def plot_days(days):
nplots = len(days)
fig, subplots = plt.subplots(nplots, figsize=(10, 50))
for i, day in enumerate(days):
subplot = subplots[i]
plot_day(day, subplot)
plt.tight_layout()
plt.savefig('./plots/best.png')
def test_pcolormesh_positive():
fig, ax = plt.subplots(1)
np.random.seed(10)
ppl.pcolormesh(
fig, ax, np.random.uniform(size=(10, 10)), xticklabels=string.uppercase[:10], yticklabels=string.lowercase[-10:]
)
def test_pcolormesh_labels():
fig, ax = plt.subplots(1)
np.random.seed(10)
ppl.pcolormesh(
fig, ax, np.random.randn(10, 10), xticklabels=string.uppercase[:10], yticklabels=string.lowercase[-10:]
)
def test_pcolormesh_labels():
fig, ax = plt.subplots(1)
np.random.seed(10)
ppl.pcolormesh(fig, ax, np.random.randn(10, 10),
xticklabels=UPPERCASE_CHARS[:10],
yticklabels=LOWERCASE_CHARS[-10:])
def plot_days(days):
nplots = len(days)
fig, subplots = plt.subplots(nplots, figsize=(10, 50))
for i, day in enumerate(days):
subplot = subplots[i]
plot_day(day, subplot)
plt.tight_layout()
plt.savefig('./plots/best.png')
def test_scatter():
# Set the random seed for consistency
np.random.seed(12)
fig, ax = plt.subplots(1)
# Show some color range
for i in range(2):
x = np.random.randn(1000)
ppl.hist(ax, x)
def test_pcolormesh_positive_other_cmap():
red_purple = brewer2mpl.get_map('RdPu', 'sequential', 8).mpl_colormap
fig, ax = plt.subplots(1)
np.random.seed(10)
ppl.pcolormesh(fig, ax, np.random.uniform(size=(10, 10)),
xticklabels=UPPERCASE_CHARS[:10],
yticklabels=LOWERCASE_CHARS[-10:],
cmap=red_purple)
def test_legend():
# Set the random seed for consistency
np.random.seed(12)
fig, ax = plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
ppl.scatter(ax, x, y, label=str(i))
ppl.legend(ax)
def __init__(self, plot, langs=['en', 'ru']):
self.plot = plot
self.langs = langs
self._set_defaults()
self.figures = {}
for lang in self.langs:
self.plot.specs['facets'] = len(self.plot.specs['countries'])
rows, cols = self._get_grid(self.plot.specs['facets'])
self.plot.specs['cols'] = cols
self.plot.specs['rows'] = rows
figure, ax_array = plt.subplots(rows, cols)
ax_array = self._hide_axes(ax_array, rows, cols)
self.figures[lang] = (figure, ax_array)
def test_plot():
# Set the random seed for consistency
np.random.seed(12)
fig, ax = plt.subplots(1)
# Show the whole color range
for i in range(8):
y = np.random.normal(size=1000).cumsum()
x = np.arange(1000)
# For now, you need to specify both x and y :(
# Still figuring out how to specify just one
ppl.plot(ax, x, y, label=str(i))
def draw_color_mesh(self):
'''
Draws a heatmap of pairs of heroes which co-occur
in the winning and in the losing teams, useful to
visualize the relationship between strong pairs of
heroes which lead to victories vs. weak pairs of
heroes which don't have much synergy
'''
red_yellow = brewer2mpl.get_map('YlGnBu', 'Sequential', 9).mpl_colormap
fig, ax = plt.subplots(1, figsize=(13, 10))
ax.set_xlim([0, self.c])
ax.set_ylim([0, self.c])
mesh = np.zeros((self.c, self.c), dtype=float)
for i in range(0, self.c):
for j in range(0, self.c):
if i >= j:
# Same hero cannot be picked twice
continue
if (i, j) in self.dwstat:
if self.ddstat[(i, j)] != 0:
k = round(
self.dwstat[(i, j)] /
float(self.ddstat[(i, j)] + self.dwstat[(i, j)]),
2)
mesh[i][j] = k
mesh[j][i] = k
# *************************************************************** #
# Code to calculate the max ratios in the heatmap
# and obtain their hero indices too
# Get the indices for the largest `num_largest` values.
num_largest = 8
indices = mesh.argpartition(mesh.size - num_largest,
axis=None)[-num_largest:]
x, y = np.unravel_index(indices, mesh.shape)
print "full:"
print "x =", x
print "y =", y
print "Largest values:", mesh[x, y]
# print "Compare to: ", np.sort(mesh, axis=None)[-num_largest:]
# **************************************************************** #
ppl.pcolormesh(fig, ax, mesh, cmap=red_yellow)
fig.savefig('../Figures/HeatMap-heroPairs.png')
plt.show()
plt.clf()
def test_pcolormesh_positive_other_cmap():
red_purple = brewer2mpl.get_map("RdPu", "sequential", 8).mpl_colormap
fig, ax = plt.subplots(1)
np.random.seed(10)
ppl.pcolormesh(
fig,
ax,
np.random.uniform(size=(10, 10)),
xticklabels=string.uppercase[:10],
yticklabels=string.lowercase[-10:],
cmap=red_purple,
)
def histogram(original, updated, bins=None, main="", save=None, log=False):
"""Plot a histogram of score improvements (updated-origianl)
Input:
original - list of original scores
updated - list of updates scores in same order as original
bins - number of bins to represent improvements
"""
#Lengths of score lists must be identical, assume in same order
assert len(original) == len(original)
#Set up bins:
if bins is not None and bins > 0:
imoprovements = {(-1,-1):0}
for i in xrange(0, len(original), bins):
improvements[(0,i+bins)] = 0
else:
improvements = {(-1,-1):0, (-5,0):0, (0,1):0, (1,25):0, (25,50):0, (50,75):0, (75,100):0, (100,125):0, (125,150):0, (150,200):0, (200,300):0, (300,400):0, (500,10000):0} #defaultdict(int)
#Calcualte improvements
for o, u in izip(original, updated):
if o>u:
improvements[(-1,-1)] += 1
continue
for lower, upper in improvements:
if lower <= int(u-o) < upper:
improvements[(lower,upper)] += 1
break
keys = sorted(improvements.keys(), key=lambda x:x[0])
values = [improvements[r] for r in keys]
fig, ax = plt.subplots()
ax.set_title(main)
ax.set_xlabel("Improvement (updated-original) bitscores")
ax.set_ylabel("log(Frequency)")
#ax.set_yscale('log')
width = 1.0
#ax.set_xticks(np.arange(len(improvements)))
#ax.set_xticklabels([l for l, u in keys])
bar(ax, np.arange(len(improvements)), values, log=log,
annotate=True, grid='y', xticklabels=[l for l, u in keys])
if save is None:
plt.show()
else:
plt.savefig(save)
def plot_prep_methods(df, prep, prepi, in_file):
"""Plot comparison between BAM preparation methods.
"""
out_file = "%s-%s.png" % (os.path.splitext(in_file)[0], prep)
cats = ["concordant", "discordant-missing-total",
"discordant-extra-total", "discordant-shared-total"]
cat_labels = {"concordant": "Concordant",
"discordant-missing-total": "Discordant (missing)",
"discordant-extra-total": "Discordant (extra)",
"discordant-shared-total": "Discordant (shared)"}
vtype_labels = {"snp": "SNPs", "indel": "Indels"}
prep_labels = {"gatk": "GATK best-practice BAM preparation (recalibration, realignment)",
"none": "Minimal BAM preparation (samtools de-duplication only)"}
caller_labels = {"ensemble": "Ensemble", "freebayes": "FreeBayes",
"gatk": "GATK Unified\nGenotyper", "gatk-haplotype": "GATK Haplotype\nCaller"}
vtypes = df["variant.type"].unique()
fig, axs = plt.subplots(len(vtypes), len(cats))
callers = sorted(df["caller"].unique())
width = 0.8
for i, vtype in enumerate(vtypes):
for j, cat in enumerate(cats):
ax = axs[i][j]
if i == 0:
ax.set_title(cat_labels[cat], size=14)
ax.get_yaxis().set_ticks([])
if j == 0:
ax.set_ylabel(vtype_labels[vtype], size=14)
vals, labels, maxval = _get_chart_info(df, vtype, cat, prep, callers)
ppl.bar(ax, left=np.arange(len(callers)),
color=ppl.set2[prepi], width=width, height=vals)
ax.set_ylim(0, maxval)
if i == len(vtypes) - 1:
ax.set_xticks(np.arange(len(callers)) + width / 2.0)
ax.set_xticklabels([caller_labels[x] for x in callers], size=8, rotation=45)
else:
ax.get_xaxis().set_ticks([])
_annotate(ax, labels, vals, np.arange(len(callers)), width)
fig.text(.5, .95, prep_labels[prep], horizontalalignment='center', size=16)
fig.subplots_adjust(left=0.05, right=0.95, top=0.87, bottom=0.15, wspace=0.1, hspace=0.1)
#fig.tight_layout()
fig.set_size_inches(10, 5)
fig.savefig(out_file)
return out_file
def hero_stats_bar(self, thresh):
'''
Creates a bar chart of the heroes with highest
Wins / #Games played ratio
'''
# Create individual hero wins and losses
self.hero_stats()
fig, ax = plt.subplots(1, figsize=(9, 7))
# Compute the ratio of #Wins to the #Games played by that hero
# Most relevant statistic, better than W/L Ratio, better than
# just wins, all of them can be statistically insignificant
# in edge cases, but this can be the least of all
val = [
(k, self.wstat[k] / float(self.dstat[k] + self.wstat[k]))
for k in self.wstat
if self.wstat[k] / float(self.dstat[k] + self.wstat[k]) >= thresh
]
plt.title('Hero ID vs. Win Ratio (Matches from 01/23 - 02/24)')
plt.xlabel('Hero ID')
plt.ylabel('Win Ratio')
ax.set_xlim([0, len(val)])
ann = [round(k[1], 2) for k in val]
# Extract the xticklabels
xtl = [k[0] for k in val]
# Extract the individual values to be plotted
val = [k[1] for k in val]
ppl.bar(ax,
np.arange(len(val)),
val,
annotate=ann,
xticklabels=xtl,
grid='y',
color=ppl.colors.set2[2])
fig.savefig('../Figures/HIDvs#Wins#Games.png')
plt.show()
plt.clf()
## DO principales + vitalicios
## numero de socios por categoria
numero_socios = [str(len(principales) + len(vitalicios)),
str(250-len(principales)-len(vitalicios))]
print "Socios privilegiados con el voto " + str(len(principales) +
len(vitalicios) + len(activos))
y = [princi_money + vitali_money, activo_money + otros_money]
annotate = [locale.format("%d", y[0], grouping=True) + " S/.",
locale.format("%d", y[1], grouping=True) + " S/."]
width = 0.35
bar_color = ["r", "#66c2a5"]
plt.rc('font', **{'family': 'DejaVu Sans'})
fig, ax = plt.subplots(1, figsize=(8,6))
ind = np.arange(2)
xdata = ind + 0.05 + width
ax.bar(ind, y)
ax.set_xticks(ind + 0.4)
ax.set_xticklabels(["principales y vitalicios\n(" + numero_socios[0] + " socios)",
"otros socios\n(" + numero_socios[1] + " socios)",
],
rotation="horizontal", multialignment="center")
ax.autoscale()
ax.set_title(u'Ganancias de socios principales y vitalicios\n comparados con el resto de socios',
fontdict = {'fontsize':22}
)
y_labels = ["0", "1,000,000", "2,000,000", "3,000,000", "4,000,000",
"5,000,000", "6,000,000", "7,000,000", "8,000,000"]
out = np.zeros(n_datos)
out[:tau] = lambda_1
out[tau:] = lambda_2
return out
observation = pm.Poisson("obs", lambda_, value=datos, observed=True)
model = pm.Model([observation, lambda_1, lambda_2, tau])
mcmc = pm.MCMC(model)
mcmc.sample(50000, 10000, 1)
lambda_1_samples = mcmc.trace('lambda_1')[:]
lambda_2_samples = mcmc.trace('lambda_2')[:]
tau_samples = mcmc.trace('tau')[:]
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1)
plt.rc('font', **{'family': 'DejaVu Sans'})
plt.subplot(311)
plt.title(u'''Distribución posterior de las variables
$\lambda_1,\;\lambda_2,\;tau$''')
plt.hist(lambda_1_samples, histtype="stepfilled", bins=30, alpha=0.85,
normed=True)
plt.xlim([150000,250000])
plt.xlabel("valor de $\lambda_1$")
plt.subplot(312)
#ax.set_autoscaley_on(False)
plt.hist(lambda_2_samples, histtype="stepfilled", bins=30, alpha=0.85,
normed=True)
x.append(row[1])
counter = collections.Counter(x)
x = []
y = []
for i in sorted(counter, key=counter.get, reverse=True):
if counter[i] != 1:
x.append(i)
y.append(counter[i])
for i in range(len(x)):
print str(y[i]) + "," + str(x[i])
plt.rc('font', **{'family': 'DejaVu Sans'})
fig, ax = plt.subplots(1, figsize=(20,6))
width = 0.35
ind = np.arange(len(y))
xdata = ind + 0.05 + width
ax.bar(ind, y)
ax.set_xticks(ind + 0.5)
ax.set_xticklabels(x, rotation="vertical")
ax.autoscale()
ax.set_title(u'Ranking de canciones "Top 10"\n Radio Inspiración FM',
fontdict = {'fontsize':24}
)
plt.ylabel('Frecuencia en "Top 10"', fontdict={'fontsize':18})
plt.xlabel(u"Canción", fontdict={'fontsize':22})
import prettyplotlib as ppl
import numpy as np
from prettyplotlib import plt
fig, ax = plt.subplots(1)
np.random.seed(14)
# 'y' for make a grid based on where the major ticks are on the y-axis
ppl.bar(ax, np.arange(10), np.abs(np.random.randn(10)), grid='y')
# fig.savefig('bar_prettyplotlib_grid.png')
plt.show()
def test_bar_annotate_user():
fig, ax = plt.subplots(1)
np.random.seed(14)
ppl.bar(ax, np.arange(10), np.abs(np.random.randn(10)),
annotate=range(10,20))
def graph(fname, analysis_fns = FN_TUPLES):
"""
Graphs mean and median of vector functions over a given starting
percentage of relevant sentence ids.
Arguments:
fname -- the filename of the pickled output
analysis_fns -- a list of (fn_name, fn) to run over the data
"""
with open(fname) as f:
results = pickle.load(f)
test_set = results['test_set']
vector_functions = results['vector_functions']
step_percentage = results['step_percentage']
results = results['results']
percents = sorted(results.keys())
for fn_name, fn in analysis_fns:
fig, ax = plt.subplots(1)
ax.set_title(fn_name.capitalize())
for vec_fn in vector_functions:
xs = sorted(results.keys())
x_axis = [x * 100 for x in xs]
y_axis = [fn(results[percent][vec_fn]['recalls']) * 100 for percent in xs]
ppl.plot(ax, x_axis, y_axis, label=vec_fn, linewidth=2, color=COLORS[vec_fn])
ax.set_xticks([i * 100 for i in percents])
ax.set_xlim([percents[0]*100, percents[-1]*100])
ax.set_xlabel('Starting (%)')
ax.set_ylabel('Recall (%)')
if fn_name == 'std':
ax.set_ylabel('Std')
# shink current axis's height by 10% on the bottom for legend
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.1,
box.width, box.height * 0.9])
# put a legend below current axis
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1),
fancybox=True, shadow=True, ncol=5)
fig.savefig('line_plot_{}_{}.png'.format(fn_name, test_set))
fig, ax = plt.subplots(1)
ax.set_title('Recalls')
# jitter to see all series
current = -(len(vector_functions) / 2)
step = 1
for vec_fn in vector_functions:
# intermediate variables are for plebs
x, y = zip(*[item for sublist in [[((percent*100)+current, val*100) for val in results[percent][vec_fn]['recalls']] for percent in percents] for item in sublist])
current += step
ppl.scatter(ax, x, y, label=vec_fn, facecolor=COLORS[vec_fn], alpha=0.3)
# set x ticks and limit ranges
ax.set_xticks([i * 100 for i in percents])
ax.set_xlim([(percents[0] - step_percentage) * 100, (percents[-1] + step_percentage) * 100])
ax.set_ylim([-5,100])
ax.set_xlabel('Starting (%)')
ax.set_ylabel('Recall (%)')
# Shink current axis's height by 10% on the bottom
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.1,
box.width, box.height * 0.9])
# Put a legend below current axis
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1),
fancybox=True, shadow=True, ncol=5)
fig.savefig('scatter_plot_{}.png'.format(test_set))
def scatter(original, updated, main="", save=None):
"""Plot a scatterplot of updated bitscores vs. original bitscores
"""
#Remove hits with no improvement and calcate the number of hits with no
#improvement(udated == original), positive imporvent (updated > original),
#and negative improvment (updated < original)
print len(original)
positiveImprovement = []
negativeImprovement = []
noImprovement = 0
for o, u in izip(original, updated):
if int(o) == int(u):
noImprovement +=1
elif u > o:
positiveImprovement.append((o,u))
elif u < o:
negativeImprovement.append((o,u))
else:
noImprovement +=1
if not positiveImprovement:
positiveImprovement = [()]
if not negativeImprovement:
negativeImprovement = [()]
print positiveImprovement
print negativeImprovement
print noImprovement
#Set deimensions
x, y = zip(*positiveImprovement+negativeImprovement)
xMax = int(round(sorted(x)[-1]/500.0)*500.0)
yMax = int(round(sorted(y)[-1]/500.0)*500.0)
sep = 500
xticks = range(0, xMax, sep)
yticks = range(0,yMax,sep)
color_cycle = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
fig, ax = plt.subplots()
ax.set_title(main)
ax.set_xlabel("Original Bitscores")
ax.set_ylabel("Updated Bitscores")
#Plot postive improvement (green, automatically by prettyplotlib)
if positiveImprovement:
ppl.scatter(ax, *zip(*positiveImprovement),
label="Positive Improvement ({} seqs)".format(len(positiveImprovement)),
color=color_cycle[0])
#Draw no improvement line
ppl.plot(ax, (0,xMax), (0,xMax), color='k', linestyle='-', linewidth=2,
label="No Improvement ({} seqs)".format(noImprovement))
#Plot negative improvement (red, automatically by prettyplotlib)
if negativeImprovement:
ppl.scatter(ax, *zip(*negativeImprovement),
label="Negative Improvement ({} seqs)".format(len(negativeImprovement)),
color=color_cycle[1])
#Draw labels
ppl.legend(ax)
#Set axis
ax.set_ylim([0,yMax])
ax.set_xlim([0,xMax])
if save is None:
plt.show()
else:
pp = PdfPages(save)
pp.savefig(fig)
pp.close()
def cluster_and_render(conf,
dbname,
algo_fn=kmeans_algo(8),
merge_fn=take_last_merge_strategy,
outname="./text.html"):
db = sqlite3.connect(dbname)
r = db.execute("select min(year), max(year) from counts")
minyear, maxyear = r.fetchone()
def vectors():
r = db.execute("select word, year, c from counts order by word, year")
vects = defaultdict(dict)
for w,y,c in r:
l = vects[w]
l[y] = c
ret = []
words = []
for w in vects:
d = vects[w]
counts = [d.get(y, 0) for y in xrange(minyear, maxyear+1)]
ret.append(counts)
words.append(w)
return words, np.array(ret)
words, counts = vectors()
labels = merge_fn(algo_fn(counts))
vects = smooth(counts)
vects = np.array([[w] + list(v) for w, v in zip(words, vects)])
for v in vects[:10]:
print v
#clusterer = KMeans(nclusters, n_init=30, init='k-means++')
#data = vects[:,1:].astype(float)
#data = np.array([l / max(l) for l in data ])
#clusterer.fit(data) # words x year
#labels = clusterer.labels_
xs = np.array(range(minyear, maxyear+1))
imgs = []
content = []
def add_content(subcluster, content, suffix):
fig, ax = plt.subplots(1, figsize=(6.5,2.5))
for childax in ax.get_children():
if isinstance(childax, mpl.spines.Spine):
childax.set_color('#aaaaaa')
for i in ax.get_xticklabels():
i.set_color('#aaaaaa')
for i in ax.get_yticklabels():
i.set_color('#aaaaaa')
subcluster = sorted(subcluster, key=lambda t: max(t[1:].astype(float)), reverse=True)[:10]
subcluster = np.array(subcluster)
words = subcluster[:,0]
ys = subcluster[:,1:].astype(float)
mean = [np.mean(ys[:,i]) for i in xrange(ys.shape[1])]
ys = ys.transpose()
ax.set_ylim(top=max(10, max(map(max, ys))))
ppl.plot(ax, xs, ys, alpha=0.3, color="#7777ee")
ppl.plot(ax, xs, mean, alpha=1, color="black")
fname = './plots/%s_%s.png' % (conf, suffix)
fig.savefig(fname, format='png')
maxes = map(max, ys)
idx = maxes.index(max(maxes))
content.append(('', words, fname, idx))
for label in set(labels):
fig, ax = plt.subplots(1, figsize=(13, 5))
idxs = labels == label
cluster = vects[idxs]
cluster = sorted(cluster, key=lambda t: max(t[1:].astype(float)), reverse=True)
cluster = filter(lambda l: sum(map(float, l[1:])) > 4, cluster)
if len(cluster) < 10: continue
cluster = np.array(cluster)
words = cluster[:,0]
words = list(words)
#if 'crowd' in words:
# data = cluster[:,1:].astype(float)
# data = np.array([l / max(l) for l in data])
# clusterer = KMeans(2)
# clusterer.fit(data)
# for newlabel in set(clusterer.labels_):
# idxs = clusterer.labels_ == newlabel
# subcluster = cluster[idxs]
# add_content(subcluster, content, "%s-%s" % (label, newlabel))
# continue
cluster = cluster[:10]
add_content(cluster, content, label)
content.sort(key=lambda c: c[-1])
from jinja2 import Template
template = Template(file('./clustertemplate.html').read())
with file(outname, 'w') as f:
f.write( template.render(content=content))
def main():
description = """Simple bar plot of table. Input is a table from a file or
standard input."""
parser = argparse.ArgumentParser(
description=description,
formatter_class=RawTextHelpFormatter
)
parser.add_argument(
'-f',
'--filename',
action='store',
metavar='table.csv',
help='''A table of the format:
x_title, y_title, Main_title
x_label1, value1
x_label2, value2''',
required=False,
dest='filename',
)
args = parser.parse_args()
if args.filename:
filename = args.filename.strip()
else:
parser.print_help()
sys.exit(1)
y = []
x = []
for line in open(filename, "r").readlines():
line = line.strip()
res = re.search("[0-9]+", line)
if not res:
line = line.split(",")
x_lab = line[0]
y_lab = line[1]
main = line[2]
else:
line = line.split(",")
if len(line) < 2:
y.append(float(line[0]))
else:
x.append(line[0])
y.append(float(line[1]))
print(line)
if len(x) < 1:
x = range(1, len(y) + 1)
plt.rc('font', **{'family': 'DejaVu Sans'})
fig, ax = plt.subplots(1, figsize=(8, 6))
width = 0.2
ind = np.arange(len(y))
xdata = ind + 0.05 + width
ax.bar(ind, y)
ax.set_xticks(ind + 0.5)
ax.set_xticklabels(x)
ax.autoscale()
ax.set_title(
main,
fontdict={'fontsize': 20},
)
plt.ylabel(y_lab, fontdict={'fontsize': 15})
plt.xlabel(x_lab, fontdict={'fontsize': 15})
plt.tick_params(axis="y", which="major", labelsize=10)
plt.tick_params(axis="x", which="major", labelsize=10)
ppl.bar(ax, np.arange(len(y)), y, grid="y")
plt.tight_layout()
print("The plot has been saved as ``out.png``")
fig.savefig("out.png")
def test_bar_xticklabels():
fig, ax = plt.subplots(1)
np.random.seed(14)
n = 10
ppl.bar(ax, np.arange(n), np.abs(np.random.randn(n)),
xticklabels=string.uppercase[:n])
for x in range(count):
data[locat].append(value)
print("data")
for i in data:
print(i[:10])
print("data")
#np.random.seed(10)
#data = np.random.randn(8, 4)
u_labels = ['1e-7 mid','1e-7 end','1e-6 mid','1e-6 end','1e-5 mid','1e-5 end','1e-4 mid','1e-4 end','0.001 mid','0.001 end','0.005 mid','0.005 end','0.01 mid','0.01 end',\
'0.05 mid', '0.05 end','0.1 mid','0.1 end']
fig, ax = plt.subplots()
ax.set_xticklabels(u_labels)
ax.set_xticks(u_labels)
ax.set_xlabel('Initial Mutation Rates', fontsize=9)
ax.set_ylabel('End Proliferation Rates', fontsize=9)
i = 0
print(ax.get_xticklabels())
ppl.boxplot(ax, data) #,xticklabels=u_labels)
plt.title("Distribution of End Proliferation Rates by Initial Mutation Rate",
fontsize=9)
fig.savefig(opt.output + '_boxplot.png')
print(opt.output + "DONE")
#ppl.hist(ax,data)
#fig.savefig('histogram_prettyplotlib_default.png')
out[:tau] = lambda_1
out[tau:] = lambda_2
return out
observation = pm.Poisson("obs", lambda_, value=datos, observed=True)
model = pm.Model([observation, lambda_1, lambda_2, tau])
mcmc = pm.MCMC(model)
mcmc.sample(50000, 10000, 1)
lambda_1_samples = mcmc.trace('lambda_1')[:]
lambda_2_samples = mcmc.trace('lambda_2')[:]
tau_samples = mcmc.trace('tau')[:]
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1)
plt.rc('font', **{'family': 'DejaVu Sans'})
plt.subplot(311)
plt.title(u'''Distribución posterior de las variables
$\lambda_1,\;\lambda_2,\;tau$''')
plt.hist(lambda_1_samples,
histtype="stepfilled",
bins=30,
alpha=0.85,
normed=True)
plt.xlim([150000, 250000])
plt.xlabel("valor de $\lambda_1$")
plt.subplot(312)
#ax.set_autoscaley_on(False)
def test_bar_grid():
fig, ax = plt.subplots(1)
np.random.seed(14)
ppl.bar(ax, np.arange(10), np.abs(np.random.randn(10)), grid='y')
author = author[0].capitalize() + " " + author[1].capitalize()
if author in apdayc:
bar_color.append("r")
else:
bar_color.append("#66c2a5")
money = i[1].split(" ")
total_money = money[len(money)-1]
x.append(author)
y.append(total_money)
y = map(float, y)
plt.rc('font', **{'family': 'DejaVu Sans'})
fig, ax = plt.subplots(1, figsize=(40,6))
width = 0.35
ind = np.arange(len(y))
xdata = ind + 0.05 + width
ax.bar(ind, y)
ax.set_xticks(ind + 0.5)
ax.set_xticklabels(x, rotation="vertical")
ax.autoscale()
ax.set_title(u'Los Asociados de APDAYC que cobraron más dinero en el 2012\nen color rojo figuran los miembros del Consejo Directivo',
fontdict = {'fontsize':24}
)
y_labels = ["0", "200,000", "400,000", "600,000", "800,000", "1,000,000"]
ax.set_yticklabels(y_labels)
def test_bar_xticklabels():
fig, ax = plt.subplots(1)
np.random.seed(14)
n = 10
ppl.bar(ax, np.arange(n), np.abs(np.random.randn(n)),
xticklabels=UPPERCASE_CHARS[:n])
def test_pcolormesh():
fig, ax = plt.subplots(1)
np.random.seed(10)
ppl.pcolormesh(fig, ax, np.random.randn(10, 10))