def binned_cummulative(cummulative_per_country, bins=4, show=False):
binned_cum_per_country = dict((option,{}) for option in options)
for option in options:
for country, samples in cummulative_per_country[option].iteritems():
hist = np.histogram(samples,bins,weights=samples)[0] / np.histogram(samples,bins)[0]
binned_cum_per_country[option][country] = sorted(hist)
f, ax = plt.subplots(2)
ticks_labels = cummulative_per_country['absolute'].keys()
ticks_positions = [[] for i in range(len(ticks_labels))]
for subplot, option in enumerate(options):
width = 0.75
ind = 0
gap = 1
for i, (country, samples) in enumerate(binned_cum_per_country[option].iteritems()):
for sample in samples:
ax[subplot].bar(ind+i*gap, sample, width, color=cm.Vega20(i+1))
ticks_positions[i].append(ind+i*gap)
ind += 1
centers = [(ticks_positions[i][0] + ticks_positions[i][-1])/2 for i in range(len(ticks_labels))]
ax[subplot].set_xticks(centers)
ax[subplot].set_xticklabels(ticks_labels)
ax[0].set_title('Normalized binned amount of resistant bacterias per sample per country')
ax[1].set_title('Absolute binned number of resistant bacterias per sample per country')
plt.tight_layout()
if show:
plt.show()
def cummulative(cummulative_per_country, show=False):
f, ax = plt.subplots(2)
ticks_labels = cummulative_per_country['absolute'].keys()
ticks_positions = [[] for i in range(len(ticks_labels))]
for subplot, option in enumerate(options):
width = 0.75
ind = 0
gap = 5
for i, (country, samples) in enumerate(cummulative_per_country[option].iteritems()):
for j, sample in enumerate(samples):
ax[subplot].bar(ind+i*gap, sample, width, color=cm.Vega20(i+1))
ticks_positions[i].append(ind+i*gap)
ind += 1
centers = [(ticks_positions[i][0] + ticks_positions[i][-1])/2 for i in range(len(ticks_labels))]
ax[subplot].set_xticks(centers)
ax[subplot].set_xticklabels(ticks_labels)
ax[0].set_title('Normalized amount of resistant bacterias per sample per country')
ax[1].set_title('Absolute number of resistant bacterias per sample per country')
plt.tight_layout()
if show:
plt.show()
web_get001txtFg = False # @zt_web.web_get001txtFg #-----bs4.findall bs_get_ktag_kstr = '' #--colors #10,prism,brg,dark2,hsv,jet #10,,hot,Vega10,Vega20 cors_brg = cm.brg(np.linspace(0, 1, 10)) cors_hot = cm.hot(np.linspace(0, 1, 10)) cors_hsv = cm.hsv(np.linspace(0, 1, 10)) cors_jet = cm.jet(np.linspace(0, 1, 10)) cors_prism = cm.prism(np.linspace(0, 1, 10)) cors_Dark2 = cm.Dark2(np.linspace(0, 1, 10)) cors_Vega10 = cm.Vega10(np.linspace(0, 1, 10)) cors_Vega20 = cm.Vega20(np.linspace(0, 1, 10)) #------str.xxx sgnSP4 = ' ' sgnSP8 = sgnSP4 + sgnSP4 #-----FN.xxx logFN = '' #--------------dir # rdat0 = 'zDat/' rdatCN = rdat0 + "cn/day/" rdatCNX = rdat0 + "cn/xday/" rdatInx = rdat0 + "inx/" rdatMin0 = rdat0 + "min/"
for country, drugs in drugs_per_country.iteritems():
averages[country] = np.mean(drugs)
ticks_labels = [country for country, avg in averages.iteritems()]
ticks_positions = [[] for i in range(len(ticks_labels))]
# [country_drug_use_df['country_2letter'].ix[i] for i in range(9)]
f, ax = plt.subplots(1)
width = 0.75
ind = 0
gap = 0.5
for i, (country, avg) in enumerate(averages.iteritems()):
print country, avg
ax.bar(ind+i*gap, avg, width, color=cm.Vega20(i+1))
ticks_positions[i].append(ind+i*gap)
ind += 1
centers = [(ticks_positions[i][0] + ticks_positions[i][-1])/2 for i in range(len(ticks_labels))]
ax.set_xticks(centers)
ax.set_xticklabels(ticks_labels)
ax.set_title('Average drugs use per country')
plt.tight_layout()
plt.show()
if show:
plt.show()
def fill_square(x, y, s):
plt.fill([x, x, x + s, x + s], [y, y + s, y + s, y],
color=colormap.Vega20(rand()))