def select_widget(Select_numeric_variable):
"""
This program must take in a variable passed from the widget and turn it into a chart.
The input is known as num_var and it is the variable you must use to get the data and build a chart.
The output must return a HoloViews Chart.
"""
color = next(colors)
overlay = hv.NdOverlay({group: hv.Distribution(np.histogram(dft[dft[dep]==group][Select_numeric_variable].values)) for i,group in enumerate(target_vars)})
hv_look = overlay.opts(opts.Distribution(alpha=0.5, height=height_size, width=width_size)).opts(
title='KDE (Distribution) Plots of all Numeric Variables by Classes').opts(
xlabel='%s' %dep).opts(ylabel='%s' %Select_numeric_variable)
return hv_look
# %%
import cProfile
import line_profiler
import holoviews as hv
import numpy as np
from holoviews import opts
from guess import RcaHmm
hv.extension("bokeh", "matplotlib")
opts.defaults(opts.Distribution(width=650))
series = np.genfromtxt("data/1995/RCAmatrix1995.txt")
model = RcaHmm(series, 4)
hv.Distribution(np.log(series[series.nonzero()]))
# %%
model.baum_welch(series, 6)
hv.Curve(model.lk, "iterations", "likelihood")
# %%
gen_states = np.genfromtxt("gen_param/states.txt")
right_states = 100 * np.count_nonzero(
gen_states == model.viterbi(series)) / series.size
right_viterbi = 100 * np.count_nonzero(
gen_states == model.states(series)) / series.size
print(f"Right states with Viterbi: {right_states:.2f}%\n"
f"Right states with gamma argmax: {right_viterbi:.2f}%")
# %%
import holoviews as hv
import numpy as np
from holoviews import opts
from scipy.stats import norm
from guess import RcaHmm, import_rca_data, save_country_model
hv.extension("bokeh", "matplotlib")
opts.defaults(opts.Distribution(width=650), opts.Curve(width=650))
a, countries = import_rca_data("./mcps export fixed size/")
# %%
country = "Canada"
country_series = a[countries[country], ...]
n_nonzero = 1 - np.count_nonzero(country_series) / np.prod(
country_series.shape)
print(f"Null values: {100 * n_nonzero:.2f}%")
country_model = RcaHmm(country_series, 4)
country_model.baum_welch(country_series, eps=8)
print(
"Matrix:",
country_model.matrix,
"Means and std devs:",
country_model.distr_params,
"Zero distribution:",
country_model.zero_distr,
"Starting distribution:",
def plot_perforacion():
elementos_perforacion=serie_resumen[['dias_perforacion','Qi_hist','estado_actual']]
tabla_perforacion = hv.Table(elementos_perforacion,'pozo')
tabla_perforacion.opts(height=500,width=400,fontscale=20)
dist = hv.Distribution(serie_resumen.dias_perforacion,
label='Dias de perforacion - Función de Probabilidad')
hist=serie_resumen.dias_perforacion.dropna()
hist=np.histogram(hist)
plot_hist = hv.Histogram(hist)
#kde = univariate_kde(dist,
# bin_range=(0, serie_resumen.dias_perforacion.max()),
# bw_method='scott',
# n_samples=1000)
#kde
scatter = hv.Scatter(serie_resumen,
kdims=['dias_perforacion','profundidad_total'],
label='Dias de perforacion vs Profundidad total')
#dist = dists.redim.label(dias_perforacion='Dias de perforacion')
scatter = scatter.redim.label(dias_perforacion='Dias de perforacion', profundidad_total='Profundidad total')
tiempos = tabla_perforacion + dist + scatter
tiempos.opts(
opts.Distribution(height=500, width=700, xaxis=True,
xlabel='Dias de Perforacion',
xlim=(0,serie_resumen.dias_perforacion.max()),
line_width=1.00,
color='grey',
alpha=0.5,
fontscale=1.5,
tools=['hover']),
opts.Scatter(height=500,
width=700,
xaxis=True,
yaxis=True,
size=dim('Qi_hist')*50,
line_width=0.25,
color='estado_actual',
cmap='Set1',
fontscale=1.5,
legend_position='bottom_right'))
#fill_color=factor_cmap('estado_actual', palette=Spectral6, factors=elementos_tipos['tipo']))
tiempos
hv.output(tiempos, backend='bokeh', fig='html')
hv.save(tiempos, 'curvas_tipo.html')
#hv.save(tiempos, 'tiempos.html')
return
label='Dias de perforacion vs Profundidad total')
#dist = dists.redim.label(dias_perforacion='Dias de perforacion')
scatter = scatter.redim.label(dias_perforacion='Dias de perforacion',
profundidad_total='Profundidad total')
tiempos = dist + scatter
tiempos
tiempos.opts(
opts.Distribution(height=400,
width=800,
xaxis=True,
xlabel='Dias de Perforacion',
xlim=(0, serie_resumen.dias_perforacion.max()),
line_width=1.00,
color='grey',
alpha=0.5,
tools=['hover']),
opts.Scatter(height=400,
width=800,
xaxis=True,
yaxis=True,
size=10,
line_width=0.25,
color='orange')).cols(1)
hv.save(tiempos, 'output_tiempos.html', backend='bokeh')
###### HV Plot