if __name__ == '__main__':
from datavyz import ge
# LOADING THE DATA
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
# PERFORMING PCA
from sklearn.decomposition import PCA as sklPCA
pca = sklPCA(n_components=4)
pca.fit_transform(data['data'])
# PLOT
fig, AX = ge.components_plot(pca.components_)
ge.savefig(fig, 'docs/components-plot.png')
ge.show()
from sklearn.datasets import load_iris
dataset = load_iris()
fig, ax = ge.parallel_plot(
dataset['data'],
SET_OF_LABELS=[
'sepal length\n(cm)', 'sepal width\n(cm)', 'petal length\n(cm)',
'petal width\n(cm)'
],
COLORS=[ge.viridis(x / 2) for x in dataset['target']])
for i, name in enumerate(dataset['target_names']):
ge.annotate(ax,
name, ((i + 1) / 3., 1.1),
color=ge.viridis(i / 2),
xlim=LIMS[i],
ylim=LIMS[j],
num_xticks=3,
num_yticks=3,
yticks_labels=[],
xticks_labels=[])
graph.bar_legend(fig,
inset=inset,
X=np.arange(len(significance)+1),
colormap=mymap,\
ticks_labels=['n.s.', '$<$0.1', '$<$0.02', '$<$0.001'],
label='Significance \n \n (p, Pearson correl.)')
return fig
if __name__ == '__main__':
from datavyz import ge
# building random data
data = {}
for i in range(5):
data['feature_%s' % (i + 1)] = np.random.randn(30)
# plot
fig = ge.cross_correl_plot(data, features=list(data.keys())[:7])
ge.savefig(fig, 'docs/cross-correl.png')
a = (1-SCALE['left']-SCALE['right'])/x_plots-SCALE['wspace']
a0 = (1-SCALE0['left']-SCALE0['right'])/x_plots-SCALE0['wspace']
b = (1-SCALE['top']-SCALE['bottom'])/y_plots-SCALE['hspace']
b0 = (1-SCALE0['top']-SCALE0['bottom'])/y_plots-SCALE0['hspace']
return {
'figsize':(\
A0_format['width']*A0_ratio*x_plots,
A0_format['height']*A0_ratio*y_plots*height_to_width)}
if __name__=='__main__':
from datavyz import ge
fig, ax = ge.figure(figsize=(1.2,1), left=1., right=4.)
ax2 = ax.twinx()
ax.plot(np.log10(np.logspace(-2.2,3,100)), np.exp(np.random.randn(100)), 'o', ms=2, color=ge.blue)
ax2.plot(np.log10(np.logspace(-2,3,100)), np.exp(np.random.randn(100)), 'o', ms=1, color=ge.red)
ge.set_plot(ax2, ['right'],
yscale='log',
ylabel='blabla',
# yticks_rotation=20, size='x-small',
tck_outward=2, ycolor=ge.red)
ge.set_plot(ax, ycolor=ge.blue, xcolor='k',
yscale='log', xscale='already-log10', ylabel='blabal',
tck_outward=2, xlabel='trying', ylabelpad=-5)
ge.savefig(fig, 'docs/twin-log-scale.png')
ge.show()
return fig, ax, pval
if __name__ == '__main__':
from datavyz import ge
# fig1, _ = ge.unrelated_samples_two_conditions_comparison(\
# np.random.randn(10)+1.4, np.random.randn(12)+1.4,
# xticks_labels=['$\||$cc($V_m$,$V_{ext}$)$\||$', '$cc(V_m,pLFP)$'],
# xticks_rotation=75)
fig, ax, pval = ge.related_samples_two_conditions_comparison(
np.random.randn(10) + 2.,
np.random.randn(10) + 2.,
xticks_labels=['Ctrl', 'Test'],
xticks_rotation=45)
ge.savefig(fig, 'docs/related-samples.png')
fig, ax, pval = ge.unrelated_samples_two_conditions_comparison(
np.random.randn(10) + 2.,
np.random.randn(10) + 2.,
xticks_labels=['Ctrl', 'Test'],
xticks_rotation=45)
ge.savefig(fig, 'docs/unrelated-samples.png')
# # ge.bar(np.random.randn(5), yerr=.3*np.random.randn(5), bottom=-3, COLORS=ge.colors[:5])
ge.show()
ms=ms,
elinewidth=elw,
label=label)
else:
for x, y, sx, sy, c, label in zip(X, Y, sX, sY, COLORS, LABELS):
ax.errorbar(x,
y,
xerr=sx,
yerr=sy,
color=c,
marker=marker,
lw=lw,
ms=ms,
elinewidth=elw,
label=label)
if __name__ == '__main__':
from datavyz import ge
fig, ax = ge.scatter(Y=np.random.randn(4, 10),
sY=np.random.randn(4, 10),
xlabel='xlabel (xunit)',
ylabel='ylabel (yunit)',
title='datavyz demo plot')
ge.savefig(fig, 'docs/scatter.png')
# two_variable_analysis(np.random.randn(10), np.random.randn(10),
# colormap=viridis)
label='mean', color=graph.purple)
# # max of power over intervals
AX[1][1].plot(np.abs(coefs).max(axis=1), freqs,\
label='max.', color=graph.red)
graph.set_plot(AX[1][1], xlabel=envelope_label)
graph.legend(AX[1][1], loc=(0.1, 1.))
return fig, AX
if __name__ == '__main__':
from datavyz import ge
from analyz.freq_analysis.wavelet_transform import my_cwt # continuous wavelet transform
dt, tstop = 1e-4, 1.
t = np.arange(int(tstop / dt)) * dt
freq1, width1, freq2, width2, freq3, width3 = 10., 100e-3, 40., 40e-3, 70., 20e-3
data = 3.2+np.cos(2*np.pi*freq1*t)*np.exp(-(t-.5)**2/2./width1**2)+\
np.cos(2*np.pi*freq2*t)*np.exp(-(t-.2)**2/2./width2**2)+\
np.cos(2*np.pi*freq3*t)*np.exp(-(t-.8)**2/2./width3**2)
# Continuous Wavelet Transform analysis
freqs = np.linspace(1, 90, 40)
coefs = my_cwt(data, freqs, dt)
fig, AX = ge.time_freq_plot(t, freqs, data, coefs)
ge.savefig(fig, 'docs/time-freq.png')
LIMS = [[np.inf, -np.inf] for f in features]
for i, key_i in enumerate(features):
hist(graph, data[key_i], ax=np.ravel(AX)[i],
ylabel='', xlabel=key_i, axes_args={'spines':'bottom'})
for i in range(len(features), len(np.ravel(AX))):
np.ravel(AX)[i].axis('off')
return fig, AX
if __name__=='__main__':
from datavyz import ge
# breast cancer dataset from datavyz.klearn
from sklearn.datasets import load_breast_cancer
raw = load_breast_cancer()
data = {}
for feature, values in zip(raw['feature_names'], raw['data']):
data[feature+'\n(log)'] = np.log(values)
fig, AX = ge.features_plot(data, ms=3)
ge.savefig(fig, 'docs/features-plot.png')
ge.show()
# ax.set_xlim(xlim)
if __name__ == '__main__':
x = 32.23545345e-5
print(sci_str(x, rounding=2))
print(from_pval_to_star(x))
for i in range(20):
print(int_to_roman(i))
from datavyz import ge
fig, AX = ge.figure(axes=(10, 1), figsize=(.7, .7), bottom=1.5)
for i, ax in enumerate(AX):
ge.top_left_letter(ax, ge.int_to_roman(i + 1))
ge.matrix(np.random.randn(10, 10), ax=ax)
sax = ge.arrow(fig, [0.04, .2, .93, 0.])
ge.annotate(fig, 'time', (.5, .17), ha='center')
ge.savefig(fig, 'docs/annotations1.png')
ge.show()
# from datavyz..graphs import *
# fig, ax = figure()
# ax.plot(np.random.randn(30), np.random.randn(30), 'o')
# bar_scales(ax, xbar=2, ybar=2, location='bottom left',
# ybar_label=r'10$\mu$V', xbar_label='200ms')
# show()
alpha=alpha)
elif orientation == 'horizontal':
ax.bar(.5 * (be[1:] + be[:-1]),
hist,
width=be[1] - be[0],
edgecolor=edgecolor,
facecolor=facecolor,
lw=lw,
label=label,
alpha=alpha)
if 'xlabel' not in axes_args:
axes_args['xlabel'] = xlabel
if 'ylabel' not in axes_args:
axes_args['ylabel'] = ylabel
graph.set_plot(ax, **axes_args)
if title != '':
graph.title(ax, title)
return fig, ax
if __name__ == '__main__':
from datavyz import ge
fig, ax = ge.hist(np.random.randn(100), xlabel='some value')
ge.savefig(fig, 'docs/hist-plot.png')
import numpy as np
from datavyz import ge
fig, ax = ge.plot(Y=np.random.randn(4, 10),
sY=np.random.randn(4, 10),
xlabel='xlabel (xunit)',
ylabel='ylabel (yunit)',
title='datavyz demo plot')
ge.savefig(fig, 'docs/demo.png')
ge.show()
# width = box.width
# height = box.height
# inax_position = ax.transAxes.transform(rect[0:2])
# transFigure = fig.transFigure.inverted()
# infig_position = transFigure.transform(inax_position)
# x = infig_position[0]
# y = infig_position[1]
# width *= rect[2]
# height *= rect[3] # <= Typo was here
# subax = fig.add_axes([x,y,width,height],facecolor=facecolor)
# # x_labelsize = subax.get_xticklabels()[0].get_size()
# # y_labelsize = subax.get_yticklabels()[0].get_size()
# # x_labelsize *= rect[2]**0.5
# # y_labelsize *= rect[3]**0.5
# # subax.xaxis.set_tick_params(labelsize=x_labelsize)
# # subax.yaxis.set_tick_params(labelsize=y_labelsize)
# return subax
if __name__ == '__main__':
from datavyz import ge
y = np.exp(np.random.randn(100))
fig, ax = ge.plot(y, xlabel='time', ylabel='y-value')
sax = ge.inset(ax, [.5, .8, .5, .4])
sax2 = ge.inset(fig, [0.1, 0.1, .3, .2])
ge.hist(y, bins=10, ax=sax, axes_args={'spines': []}, xlabel='y-value')
ge.hist(y, bins=10, ax=sax2, axes_args={'spines': []}, xlabel='y-value')
ge.savefig(fig, 'docs/inset.png')
ge.show()