def test_violinplot_beanplot():
# Test violinplot and beanplot with the same dataset.
data = anes96.load_pandas()
party_ID = np.arange(7)
labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat",
"Independent-Independent", "Independent-Republican",
"Weak Republican", "Strong Republican"]
age = [data.exog['age'][data.endog == id] for id in party_ID]
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30})
plt.close(fig)
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30})
plt.close(fig)
def test_violinplot_beanplot(close_figures):
# Test violinplot and beanplot with the same dataset.
data = anes96.load_pandas()
party_ID = np.arange(7)
labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat",
"Independent-Independent", "Independent-Republican",
"Weak Republican", "Strong Republican"]
age = [data.exog['age'][data.endog == id] for id in party_ID]
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30})
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30,
'bw_factor':.2})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels, jitter=True,
plot_opts={'cutoff_val': 5, 'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels, jitter=True, side='right',
plot_opts={'cutoff_val': 5, 'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels, jitter=True, side='left',
plot_opts={'cutoff_val': 5, 'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels,
plot_opts={'bean_legend_text': 'text'})
def test_violinplot(age_and_labels, close_figures):
age, labels = age_and_labels
fig, ax = plt.subplots(1, 1)
violinplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
def test_violinplot_beanplot():
"""Test violinplot and beanplot with the same dataset."""
data = anes96.load_pandas()
party_ID = np.arange(7)
labels = [
"Strong Democrat", "Weak Democrat", "Independent-Democrat",
"Independent-Independent", "Independent-Republican", "Weak Republican",
"Strong Republican"
]
age = [data.exog['age'][data.endog == id] for id in party_ID]
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
plt.close(fig)
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
plt.close(fig)
diff_satids = []
for ss in range(len(satlab)):
diff_satid = []
for ii in range(len(sat_imerg[ss])):
if sat_imerg[ss][ii] >= minrain or sat_pcmk[ss][ii] >= minrain:
diff_satid.append(sat_anom[ss][ii])
if ss == 5 and sat_anom[ss][ii] < -50:
pcmk_ssmis_min = sat_pcmk[ss][ii] # PCMK for that event
diff_satids.append(diff_satid)
print('PCMK: %5.1f' % pcmk_ssmis_min)
plt.figure(figsize = (scol, 0.75 * scol))
violinplot(diff_satids, ax = plt.gca(), positions = range(len(sat_anom)),
show_boxplot = False, plot_opts = {'violin_fc': '0.2',
'violin_ec': 'k', 'cutoff': True})
bp = plt.boxplot(diff_satids, positions = range(len(sat_anom)),
whis = [10, 90])
for flier in bp['fliers']:
flier.set(marker = 'x', mec = 'k', ms = 4, alpha = 1)
plt.ylim([-54, 32])
circle = Ellipse((5, min(diff_satids[5])), 0.5, 6.3, color = 'r',
fill = False)
plt.gca().add_artist(circle)
plt.xticks(range(len(diff_satids)), satlab, rotation = 'vertical')
plt.setp(plt.gca(), 'xticklabels', satlab)
plt.ylabel('%s − %s (mm / h)' % (datalab['imerg'], datalab['pcmk']))
def test_violinplot_beanplot(close_figures):
# Test violinplot and beanplot with the same dataset.
data = anes96.load_pandas()
party_ID = np.arange(7)
labels = [
"Strong Democrat", "Weak Democrat", "Independent-Democrat",
"Independent-Independent", "Independent-Republican", "Weak Republican",
"Strong Republican"
]
age = [data.exog['age'][data.endog == id] for id in party_ID]
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30,
'bw_factor': .2
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
jitter=True,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
jitter=True,
side='right',
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
jitter=True,
side='left',
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels, plot_opts={'bean_legend_text': 'text'})
P[rr, tt] = np.ma.mean([ii for ii in precip[t0 : t1][R[t0 : t1] == rr + 1]])
F[rr, tt] = np.ma.sum(R[t0 : t1] == rr + 1) / np.float(np.ma.count(R[t0 : t1]))
Pall[rr] += [ii for ii in precip[t0 : t1][R[t0 : t1] == rr + 1] if not np.ma.is_masked(ii)]
P[nreg, tt] = np.ma.mean([ii for ii in precip[t0 : t1][R[t0 : t1] < 4]])
F[nreg, tt] = np.ma.sum(R[t0 : t1] < 4) / np.float(np.ma.count(R[t0 : t1]))
Pall[nreg] += [ii for ii in precip[t0 : t1][R[t0 : t1] < 4] if not np.ma.is_masked(ii)]
t0 = t1
tt += 1
Pcon = [np.sum(Pall[rr]) / Ptotal for rr in range(nreg + 1)] # fractional contribution
x = np.arange(nreg + 1)
plt.figure(figsize = (scol, 0.5 * scol))
violinplot(Pall, ax = plt.gca(), positions = x, show_boxplot = False,
plot_opts = {'violin_fc': '0.2', 'violin_ec': 'k', 'cutoff': True})
bp = plt.boxplot(Pall, sym = '', widths = 0.4, positions = x, whis = [5, 95])
plt.setp(bp['boxes'], color = 'black')
plt.setp(bp['whiskers'], color = 'black', linestyle = '-')
plt.setp(bp['medians'], color = 'black')
plt.scatter(x, [np.mean(ii) for ii in Pall], s = 10, c = 'k')
for rr in range(nreg + 1): plt.text(x[rr], 105, '{:2.0f}%'.format(Pcon[rr] * 100), ha = 'center')
plt.ylim([0, 100])
plt.setp(plt.gca(), 'xticks', x)
plt.setp(plt.gca(), 'xticklabels', regimes + ('CR1-3',))
plt.ylabel('$P$ (mm / day)')
plt.grid(axis = 'y')
plt.axvline(2.5, c = 'k', ls = ':', lw = 0.5)
plt.savefig('Fig2.pdf')
plt.close()
for ii in range(len(sat_imerg[ss])):
if sat_imerg[ss][ii] >= minrain or sat_pcmk[ss][ii] >= minrain:
diff_satid.append(sat_anom[ss][ii])
if ss == 5 and sat_anom[ss][ii] < -50:
pcmk_ssmis_min = sat_pcmk[ss][ii] # PCMK for that event
diff_satids.append(diff_satid)
print('PCMK: %5.1f' % pcmk_ssmis_min)
plt.figure(figsize=(scol, 0.75 * scol))
violinplot(diff_satids,
ax=plt.gca(),
positions=range(len(sat_anom)),
show_boxplot=False,
plot_opts={
'violin_fc': '0.2',
'violin_ec': 'k',
'cutoff': True
})
bp = plt.boxplot(diff_satids,
positions=range(len(sat_anom)),
whis=[10, 90])
for flier in bp['fliers']:
flier.set(marker='x', mec='k', ms=4, alpha=1)
plt.ylim([-54, 32])
circle = Ellipse((5, min(diff_satids[5])), 0.5, 6.3, color='r', fill=False)
plt.gca().add_artist(circle)
use = R2[full_model] > .5
import statsmodels.graphics.boxplots as sgb
import matplotlib.patches as mpatches
fig = plt.figure(figsize=(12, 6))
fig.clf()
for j, tmod in enumerate([tested_models1, tested_models2]):
ax = fig.add_axes([.12, .65 - .45 * j, .8, .3])
for i, mod in enumerate(true_models):
for k, m in enumerate(tmod):
color = 'rgbcymk'[k]
vals = (R2[full_model] - R2[m])[use & (tgc == mod)].dropna()
if len(vals) > 3:
sgb.violinplot([vals],ax=ax,positions=[i+.2*k],show_boxplot=False,\
plot_opts=dict(violin_fc=color,violin_ec=color,violin_alpha=.5,violin_width=.15,cutoff=True))
bp = ax.boxplot([vals],
positions=[i + .2 * k],
widths=.1,
sym='',
notch=False)
plt.setp(bp['boxes'], color='k', linewidth=1)
plt.setp(bp['whiskers'],
color='k',
linestyle='solid',
linewidth=.5)
plt.setp(bp['caps'], color='k')
plt.setp(bp['medians'], color='r')
patches = [
mpatches.Patch(color='rgbcymk'[k], alpha=.5)
for k in range(len(tmod))