def default(self, o):
# TODO add more support types
if isinstance(o, set):
return {'__set__': list(o)}
elif isinstance(o, numpy.ndarray):
return {'__numpy__': _to_default_list(o)}
elif hasattr(o, '_repr_html_'):
rb = BrtcReprBuilder()
rb.addHTML(o._repr_html_())
return {
'_repr_brtc_': rb.get(),
'__pickled__': list(pickle.dumps(o))
}
elif hasattr(o, 'savefig'):
rb = BrtcReprBuilder()
rb.addPlt(o)
return {
'_repr_brtc_': rb.get(),
'__pickled__': list(pickle.dumps(o))
}
else:
rb = BrtcReprBuilder()
rb.addRawTextMD(str(o))
return {
'_repr_brtc_': rb.get(),
'__pickled__': list(pickle.dumps(o))
}
def _pairplot(table, x_vars, y_vars=None, kind='scatter', diag_kind='auto', markers=None, palette=None, height=2.5, aspect=1, dropna=True, hue=None):
s_default = plt.rcParams['lines.markersize'] ** 2.
plot_kws = {"s": s_default * height / 6.4}
if y_vars is None:
y_vars = x_vars
if kind == 'scatter':
g = sns.pairplot(table, x_vars=x_vars, y_vars=y_vars, kind=kind, diag_kind=diag_kind, markers=markers, height=height, aspect=aspect, \
dropna=dropna, hue=hue, palette=palette, plot_kws=plot_kws)
else:
scatter_kws = {'scatter_kws':plot_kws}
g = sns.pairplot(table, x_vars=x_vars, y_vars=y_vars, kind=kind, diag_kind=diag_kind, markers=markers, height=height, aspect=aspect, \
dropna=dropna, hue=hue, palette=palette, plot_kws=scatter_kws)
if height <= 2.5:
for ax in g.axes.flatten():
for label in ax.get_xticklabels():
label.set_rotation(90 * (2.5 - height))
rb = BrtcReprBuilder()
rb.addPlt(plt)
plt.clf()
return {'result': {'_repr_brtc_':rb.get()} }
def _tukeys_range_test(table, response_cols, factor_col, alpha=0.05):
rb = BrtcReprBuilder()
rb.addMD("""## Tukey's range test Result""")
for response_col in response_cols:
data = table[response_col]
posthoc = pairwise_tukeyhsd(data, table[factor_col], alpha=alpha)
posthoc_html = posthoc._results_table.as_html()
posthoc.plot_simultaneous()
rb.addMD("""### {response_col}""".format(response_col=response_col))
rb.addHTML(posthoc_html)
rb.addPlt(plt)
plt.clf()
return {'result': {'_repr_brtc_': rb.get()}}
def tukeys_range_test(table, response_cols, factor_col, alpha=0.05):
if alpha < 0.001 or alpha >= 0.9:
raise BrighticsFunctionException("0006", ['alpha', 0.001, 0.9])
rb = BrtcReprBuilder()
rb.addMD("""## Tukey's range test Result""")
for response_col in response_cols:
data = table[response_col]
posthoc = pairwise_tukeyhsd(data, table[factor_col], alpha=alpha)
posthoc_html = posthoc._results_table.as_html()
posthoc.plot_simultaneous()
rb.addMD("""### {response_col}""".format(response_col=response_col))
rb.addHTML(posthoc_html)
rb.addPlt(plt)
plt.clf()
return {'result': {'_repr_brtc_': rb.get()}}
def _correlation(table, vars, method='pearson', height=2.5, corr_prec=2):
validate(greater_than(height, 0, 'height'),
greater_than_or_equal_to(corr_prec, 1, 'corr_prec'))
size = len(vars)
s_default = plt.rcParams['lines.markersize']**2.
scatter_kws = {"s": s_default * height / 6.4}
corr_arr = np.ones((size, size)) # TODO variable name dict
p_arr = np.zeros((size, size))
for i in range(size):
for j in range(i):
if method == 'pearson':
r, p = stats.pearsonr(table[vars[i]], table[vars[j]])
elif method == 'spearman':
r, p = stats.spearmanr(table[vars[i]], table[vars[j]])
elif method == 'kendal':
r, p = stats.kendalltau(table[vars[i]], table[vars[j]])
corr_arr[i][j] = r
p_arr[i][j] = p
for i in range(size):
for j in range(i, size):
corr_arr[i][j] = corr_arr[j][i]
p_arr[i][j] = p_arr[j][i]
def corr(x, y, **kwargs):
if kwargs['method'] == 'pearson':
r, p = stats.pearsonr(x, y)
elif kwargs['method'] == 'spearman':
r, p = stats.spearmanr(x, y)
elif kwargs['method'] == 'kendal':
r, p = stats.kendalltau(x, y)
p_stars = ''
if p <= 0.05:
p_stars = '*'
if p <= 0.01:
p_stars = '**'
if p <= 0.001:
p_stars = '***'
corr_text = '{:.{prec}f}'.format(r, prec=corr_prec)
print(type(corr_prec))
font_size = abs(r) * 15 * 2 / corr_prec + 5
ax = plt.gca()
ax.annotate(corr_text, [
.5,
.5,
],
xycoords="axes fraction",
ha='center',
va='center',
fontsize=font_size * height)
ax.annotate(p_stars,
xy=(0.65, 0.6),
xycoords=ax.transAxes,
color='red',
fontsize=17 * height)
g = sns.PairGrid(table, vars=vars, height=height)
g.map_diag(sns.distplot)
if method == 'pearson':
g.map_lower(sns.regplot, scatter_kws=scatter_kws)
else:
g.map_lower(sns.regplot, lowess=True, scatter_kws=scatter_kws)
g.map_upper(corr, method=method)
rb = BrtcReprBuilder()
rb.addPlt(plt)
plt.clf()
params = {'vars': vars, 'method': method, 'height': height}
res = dict()
res['params'] = params
res['corr'] = corr_arr
res['pvalue'] = p_arr
res['_repr_brtc_'] = rb.get()
return {'result': res}
