def gluco_plot(time,
gluco,
hypo=70,
hyper=126,
title="Patiend ID glucose level"):
"""Plot the glucose level on a time span.
Parameters
-------------------
time : array of datetimes, the horizontal axis
gluco : array of float, the correspondent glucose level on the vertical axis
hypo : number, hypoglicaemia threshold in mg/dl (default is 70, i.e. 3.9 mmol/l)
hyper : number, hyperglicaemia threshold in mg/dl (default is 126, i.e. 7 mmol/l)
title : string, the title of the plot (optional)
"""
plt.figure(figsize=(10, 6))
plt.hlines(hypo,
time[0],
time[-1],
linestyles='dashed',
label='hypoglicaemia')
plt.hlines(hyper,
time[0],
time[-1],
linestyles='dotted',
label='hyperglicaemia')
plt.ylim([10, 410])
plt.plot_date(time, gluco, '-', label='glucose level')
plt.title(title)
plt.ylabel('mg/dL')
plt.xticks(rotation='vertical')
plt.legend()
def residuals(df,
forecast,
skip_first=0,
skip_last=0,
title="Patiend ID CGM",
savefig=False):
"""Plot the input residuals.
Parameters
-------------------
df : DataFrame, the output returned by gluco_extract(..., return_df=True)
forecast : array of float, the prediction for the given time-series
skip_first : number, the number of initial samples to exclude
skip_last : number, the number of final samples to exclude
title : string, the title of the plot (optional)
savefig : bool, if True save title.png
"""
# Evaluate the residuals (exclude learning and open loop ph samples)
residuals = df.as_matrix()[skip_first:-skip_last].ravel(
) - forecast[skip_first:-skip_last]
plt.figure(figsize=(12, 4), dpi=300)
plt.plot(df.index[skip_first:-skip_last], residuals)
DW = sm.stats.durbin_watson(residuals)
plt.title('Durbin-Watson: {:.3f}'.format(DW))
if savefig: plt.savefig(title + '_residuals.png')
def cgm(df,
gluco_fit=None,
hypo=70,
hyper=126,
title="Patiend ID CGM",
savefig=False):
"""Plot the CGM signal on an input time span.
Parameters
-------------------
df : DataFrame, the output returned by gluco_extract(..., return_df=True)
gluco_fit : array of float, the results of a fitted model (optional)
hypo : number, hypoglicaemia threshold in
mg/dl (default is 70, i.e. 3.9 mmol/l)
hyper : number, hyperglicaemia threshold
in mg/dl (default is 126, i.e. 7 mmol/l)
title : string, the title of the plot (optional)
savefig : bool, if True save title.png
"""
plt.figure(figsize=(10, 6), dpi=300)
plt.hlines(hypo,
df.index[0],
df.index[-1],
linestyles='dashed',
label='hypoglicaemia')
plt.hlines(hyper,
df.index[0],
df.index[-1],
linestyles='dotted',
label='hyperglicaemia')
plt.ylim([10, 410])
plt.plot_date(df.index, df.as_matrix(), '-', label='real CGM')
if gluco_fit is not None:
plt.plot(df.index, gluco_fit, '--', label='predicted CGM')
plt.title(title)
plt.ylabel('mg/dL')
plt.xticks(rotation='vertical')
plt.legend(bbox_to_anchor=(1.1, 1.0))
if savefig: plt.savefig(title + '_fit.png')
# In[36]:
# ---------- Plot ------------------------ #
plt.subplot(211)
plt.plot(ts, ys, '-', label='noisy') # noisy measures
plt.plot(ts, y, '--', label='filtered') # filtered measures
plt.grid()
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=2,
mode="expand",
borderaxespad=0.)
plt.subplot(212)
residuals = ys.ravel()[0:Ns] - y.ravel()[0:Ns]
plt.plot(ts, residuals)
plt.title('Durbin-Watson: {:.3f}'.format(sm.stats.durbin_watson(residuals)))
plt.tight_layout()
plt.grid()
# ## Translate `pykalman` names into our convention
#
# **Parameter Name** (Notation)
# - initial_state_mean (`X0`)
# - initial_state_covariance (`P0`)
# - transition_matrices (`F`)
# - transition_offsets (0?)
# - transition_covariance (`Q`)
# - observation_matrices (`X`)
# - observation_offsets (0?)
# - observation_covariance (`R`)
obs_errors = [100, 1.0, 1.0, 1.0, 0.001]
levels = [0.95, 0.835, 0.685, 0.51, 0.34, 0.2, 0.095, 0.025]
plt.rc('lines', linewidth=1.0)
plt.figure(figsize=(9, 3), facecolor='white')
dirs = ['double']
labels = ['64 bits']
handles = []
for d, l in zip(dirs, labels):
print(f'Plotting {d}')
handles += plot(d, l)
plt.ylim([0, 3.5])
plt.xlabel('Time')
plt.ylabel(f'Total analysis RMSE')
plt.title('')
# Add legend
leg = plt.legend(handles=handles, frameon=True, ncol=2)
rect = leg.get_frame()
rect.set_linewidth(0.0)
rect.set_alpha(0.7)
plt.savefig(f'total_error_compare.pdf', bbox_inches='tight')
plt.show()
import pandas as pd
from nlpia.plots import scatter_3d
np = pd.np
df = pd.DataFrame(pd.np.random.randn(1000, 10))
np = pd.np
plt.figure()
f = plt.figure()
ax = f.add_subplot(111)
axes = df.plot(kind='scatter', x=0, y=1, ax=ax)
plt.title('2D Normally Distributed')
plt.tight_layout()
plt.show()
scatter_3d(df)
plt.show()
df.values.dot(df.values)
np.do(df.values, df.values)
np.dot(df.values, df.values)
np.dot(df.values, df.values.T)
np.dot(df.values, df.values.T).shape
np.dot(df.values, df.values.T).shape.sum()
np.dot(df.values.T, df.values)
np.dot(df.values.T, df.values).shape
norms = pd.DataFrame([[np.linalg.norm(row[:i]) for i in range(2,11)] for row in df])
row