ys_entire = np.sum(common_otu_entire[np.ix_(windows_entire_ids, otus_dn_cols)], axis=1)
ys_entire_dn.append(ys_entire)
trace = go.Box(
y=ys_entire,
name="W" + str(w_id + 1),
boxpoints=box_points,
marker_color=marker_color,
line_color=line_color
)
traces_entire_dn.append(trace)
layout = go.Layout(
margin=get_margin(),
autosize=True,
showlegend=False,
xaxis=get_axis(''),
yaxis=get_axis('Cumulated Abundance')
)
fig = go.Figure(data=traces_t0_dp, layout=layout)
plotly.offline.plot(fig, filename=out_path + '/cumulated_abundance_t0_diet_positive_box_plot.html', auto_open=False, show_link=True)
plotly.io.write_image(fig, out_path + '/cumulated_abundance_t0_diet_positive_box_plot.png')
plotly.io.write_image(fig, out_path + '/cumulated_abundance_t0_diet_positive_box_plot.pdf')
fig = go.Figure(data=traces_t1_dp, layout=layout)
plotly.offline.plot(fig, filename=out_path + '/cumulated_abundance_t1_diet_positive_box_plot.html', auto_open=False, show_link=True)
plotly.io.write_image(fig, out_path + '/cumulated_abundance_t1_diet_positive_box_plot.png')
plotly.io.write_image(fig, out_path + '/cumulated_abundance_t1_diet_positive_box_plot.pdf')
fig = go.Figure(data=traces_entire_dp, layout=layout)
plotly.offline.plot(fig, filename=out_path + '/cumulated_abundance_entire_diet_positive_box_plot.html', auto_open=False, show_link=True)
def pcoa_plot(path, ord_result, common_subjects, metrics_key, metrics_dict):
coord_matrix = ord_result.samples.values.T
xs_all = coord_matrix[0]
ys_all = coord_matrix[1]
zs_all = coord_matrix[2]
traces_3d = []
traces_2d = []
for status in metrics_dict:
curr_subjects = metrics_dict[status]
xs = []
ys = []
zs = []
for subj in curr_subjects:
index = common_subjects.index(subj)
xs.append(xs_all[index])
ys.append(ys_all[index])
zs.append(zs_all[index])
color = cl.scales['8']['qual']['Set1'][list(
metrics_dict.keys()).index(status)]
coordinates = color[4:-1].split(',')
color_transparent = 'rgba(' + ','.join(coordinates) + ',' + str(
0.3) + ')'
color_border = 'rgba(' + ','.join(coordinates) + ',' + str(0.8) + ')'
trace = go.Scatter3d(x=xs,
y=ys,
z=zs,
name=status,
mode='markers',
marker=dict(size=8,
color=color_border,
line=dict(color=color_transparent,
width=0.5),
opacity=0.8))
traces_3d.append(trace)
trace = go.Scatter(x=ys,
y=xs,
name=status,
mode='markers',
marker=dict(size=8,
color=color_border,
line=dict(color=color_transparent,
width=0.5),
opacity=0.8))
traces_2d.append(trace)
layout_3d = go.Layout(margin=get_margin(),
autosize=True,
legend=get_legend())
layout_2d = go.Layout(margin=get_margin(),
autosize=True,
legend=get_legend(),
xaxis=get_axis("PC1"),
yaxis=get_axis("PC2"))
fig_3d = go.Figure(data=traces_3d, layout=layout_3d)
fig_3d.update_layout(scene=dict(
xaxis=get_axis("PC1"), yaxis=get_axis("PC2"), zaxis=get_axis("PC3")))
fig_2d = go.Figure(data=traces_2d, layout=layout_2d)
if not os.path.exists(path):
os.makedirs(path)
plotly.offline.plot(fig_3d,
filename=path + '/pcoa_3d_' + metrics_key + '.html',
auto_open=False,
show_link=True)
plotly.io.write_image(fig_3d, path + '/pcoa_3d_' + metrics_key + '.png')
plotly.io.write_image(fig_3d, path + '/pcoa_3d_' + metrics_key + '.pdf')
plotly.offline.plot(fig_2d,
filename=path + '/pcoa_2d_' + metrics_key + '.html',
auto_open=False,
show_link=True)
plotly.io.write_image(fig_2d, path + '/pcoa_2d_' + metrics_key + '.png')
plotly.io.write_image(fig_2d, path + '/pcoa_2d_' + metrics_key + '.pdf')
['Reduced Frailty', 'No change in Frailty', 'Increased Frailty']):
color = cl.scales['8']['qual']['Set1'][color_id]
coordinates = color[4:-1].split(',')
marker_color = 'rgba(' + ','.join(coordinates) + ',' + str(1.0) + ')'
ys = [
frailty_changes_count['Subject'][fc_type],
frailty_changes_count['Control'][fc_type],
]
traces.append(go.Bar(x=xs, y=ys, name=fc_type, marker_color=marker_color))
layout = go.Layout(margin=get_margin(),
autosize=True,
showlegend=True,
xaxis=get_axis(''),
yaxis=get_axis('Number of subjects'))
fig = go.Figure(data=traces, layout=layout)
plotly.offline.plot(fig,
filename=out_path + '/frailty_changes_box_plot.html',
auto_open=False,
show_link=True)
plotly.io.write_image(fig, out_path + '/frailty_changes_box_plot.png')
plotly.io.write_image(fig, out_path + '/frailty_changes_box_plot.pdf')
traces = []
xs = ['Intervention', 'Control']
for fc_type in ['Reduced Frailty', 'Increased Frailty']:
if fc_type == 'Reduced Frailty':
ys.append(float(metadata_t0[code]['compliance160']) / 160.0 * 100.0 )
color = cl.scales['8']['qual']['Set1'][countries.index(country)]
coordinates = color[4:-1].split(',')
marker_color = 'rgba(' + ','.join(coordinates) + ',' + str(0.5) + ')'
line_color = 'rgba(' + ','.join(coordinates) + ',' + str(1.0) + ')'
trace = go.Box(
y=ys,
name=country,
boxpoints='outliers',
marker_color=marker_color,
line_color=line_color
)
traces.append(trace)
layout = go.Layout(
margin=get_margin(),
autosize=True,
showlegend=False,
xaxis=get_axis(''),
yaxis=get_axis('adherence')
)
fig = go.Figure(data=traces, layout=layout)
plotly.offline.plot(fig, filename=out_path + '/adherence_t0_countries_box_plot.html', auto_open=False, show_link=True)
plotly.io.write_image(fig, out_path + '/adherence_t0_countries_box_plot.png')
plotly.io.write_image(fig, out_path + '/adherence_t0_countries_box_plot.pdf')
markers_orig_mse, markers_orig_corr = run_iterative_regressor(
markers_orig_df, adherence[target_country], markers_orig,
'Markers original')
non_markers_orig_mse, non_markers_orig_corr = run_iterative_regressor(
non_markers_orig_df, adherence[target_country], non_markers_orig,
'Non-Markers original')
traces = []
traces.append(go.Box(y=markers_orig_mse, name='Markers', boxpoints=box_points))
traces.append(
go.Box(y=non_markers_orig_mse, name='Non-Markers', boxpoints=box_points))
layout = go.Layout(margin=get_margin(),
autosize=True,
showlegend=True,
xaxis=get_axis(''),
yaxis=get_axis('MSE across iterative models at Baseline'))
fig = go.Figure(data=traces, layout=layout)
plotly.offline.plot(fig,
filename=out_path + '/mse_original_boxplot_' +
target_country + '.html',
auto_open=False,
show_link=True)
plotly.io.write_image(
fig, out_path + '/mse_original_boxplot_' + target_country + '.png')
plotly.io.write_image(
fig, out_path + '/mse_original_boxplot_' + target_country + '.pdf')
traces = []
traces.append(go.Box(y=markers_orig_corr, name='Markers',
mse[country] = run_regressor(otu_df[country], adherence[country])
rmse[country] = np.sqrt(mse[country])
traces = []
for country in countries:
ys = rmse[country]
color = cl.scales['8']['qual']['Set1'][countries.index(country)]
coordinates = color[4:-1].split(',')
marker_color = 'rgba(' + ','.join(coordinates) + ',' + str(0.5) + ')'
line_color = 'rgba(' + ','.join(coordinates) + ',' + str(1.0) + ')'
traces.append(go.Box(
y=ys,
name=country,
boxpoints=box_points
))
layout = go.Layout(
margin=get_margin(),
autosize=True,
showlegend=True,
xaxis=get_axis(''),
yaxis=get_axis('RMSE across countries at T0')
)
fig = go.Figure(data=traces, layout=layout)
plotly.offline.plot(fig, filename=out_path + '/country_t0_box_plot.html', auto_open=False, show_link=True)
plotly.io.write_image(fig, out_path + '/country_t0_box_plot.png')
plotly.io.write_image(fig, out_path + '/country_t0_box_plot.pdf')
# plot bar plots
x = ['Baseline', 'Final']
traces = []
for status in frailty_statuses:
if status in obs_dict_t0['frailty_status']:
y_t0 = len(obs_dict_t0['frailty_status'][status])
else:
y_t0 = 0
ys = [y_t0, len(obs_dict_t1['frailty_status'][status])]
traces.append(go.Bar(x=x, y=ys, name=status))
layout = go.Layout(margin=get_margin(),
autosize=True,
showlegend=True,
xaxis=get_axis(''),
yaxis=get_axis('Number of subjects'))
fig = go.Figure(data=traces, layout=layout)
plotly.offline.plot(fig,
filename=out_path + '/frailty_status_box_plot.html',
auto_open=False,
show_link=True)
plotly.io.write_image(fig, out_path + '/frailty_status_box_plot.png')
plotly.io.write_image(fig, out_path + '/frailty_status_box_plot.pdf')
f = open(path + '/' + dr_otus_source + '/diet_positive.txt')
otus_dp = f.read().splitlines()
f.close()
otus_dp_cols = np.array(
[common_otu_col_dict[x] for x in otus_dp if x in common_otu_col_dict])