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import numpy as np
import plotly.graph_objects as go
from pyVHR.signals.bvp import BVPsignal
def getErrors(bpmES, bpmGT, timesES, timesGT):
RMSE = RMSEerror(bpmES, bpmGT, timesES, timesGT)
MAE = MAEerror(bpmES, bpmGT, timesES, timesGT)
MAX = MAXError(bpmES, bpmGT, timesES, timesGT)
PCC = PearsonCorr(bpmES, bpmGT, timesES, timesGT)
return RMSE, MAE, MAX, PCC
def RMSEerror(bpmES, bpmGT, timesES=None, timesGT=None):
""" RMSE: """
diff = bpm_diff(bpmES, bpmGT, timesES, timesGT)
n,m = diff.shape # n = num channels, m = bpm length
df = np.zeros(n)
for j in range(m):
for c in range(n):
df[c] += np.power(diff[c,j],2)
# -- final RMSE
RMSE = np.sqrt(df/m)
return RMSE
def MAEerror(bpmES, bpmGT, timesES=None, timesGT=None):
""" MAE: """
diff = bpm_diff(bpmES, bpmGT, timesES, timesGT)
n,m = diff.shape # n = num channels, m = bpm length
df = np.sum(np.abs(diff),axis=1)
# -- final MAE
MAE = df/m
return MAE
def MAXError(bpmES, bpmGT, timesES=None, timesGT=None):
""" MAE: """
diff = bpm_diff(bpmES, bpmGT, timesES, timesGT)
n,m = diff.shape # n = num channels, m = bpm length
df = np.max(np.abs(diff),axis=1)
# -- final MAE
MAX = df
return MAX
def PearsonCorr(bpmES, bpmGT, timesES=None, timesGT=None):
from scipy import stats
diff = bpm_diff(bpmES, bpmGT, timesES, timesGT)
n,m = diff.shape # n = num channels, m = bpm length
CC = np.zeros(n)
for c in range(n):
# -- corr
r,p = stats.pearsonr(diff[c,:]+bpmES[c,:],bpmES[c,:])
CC[c] = r
return CC
def printErrors(RMSE, MAE, MAX, PCC):
print("\n * Errors: RMSE = %.2f, MAE = %.2f, MAX = %.2f, PCC = %.2f" %(RMSE,MAE,MAX,PCC))
def displayErrors(bpmES, bpmGT, timesES=None, timesGT=None):
if (timesES is None) or (timesGT is None):
timesES = np.arange(m)
timesGT = timesES
diff = bpm_diff(bpmES, bpmGT, timesES, timesGT)
n,m = diff.shape # n = num channels, m = bpm length
df = np.abs(diff)
dfMean = np.around(np.mean(df,axis=1),1)
# -- plot errors
fig = go.Figure()
name = 'Ch 1 (µ = ' + str(dfMean[0])+ ' )'
fig.add_trace(go.Scatter(x=timesES, y=df[0,:], name=name, mode='lines+markers'))
if n > 1:
name = 'Ch 2 (µ = ' + str(dfMean[1])+ ' )'
fig.add_trace(go.Scatter(x=timesES, y=df[1,:], name=name, mode='lines+markers'))
name = 'Ch 3 (µ = ' + str(dfMean[2])+ ' )'
fig.add_trace(go.Scatter(x=timesES, y=df[2,:], name=name, mode='lines+markers'))
fig.update_layout(xaxis_title='Times (sec)', yaxis_title='MAE', showlegend=True)
fig.show()
# -- plot bpm Gt and ES
fig = go.Figure()
GTmean = np.around(np.mean(bpmGT),1)
name = 'GT (µ = ' + str(GTmean)+ ' )'
fig.add_trace(go.Scatter(x=timesGT, y=bpmGT, name=name, mode='lines+markers'))
ESmean = np.around(np.mean(bpmES[0,:]),1)
name = 'ES1 (µ = ' + str(ESmean)+ ' )'
fig.add_trace(go.Scatter(x=timesES, y=bpmES[0,:], name=name, mode='lines+markers'))
if n > 1:
ESmean = np.around(np.mean(bpmES[1,:]),1)
name = 'ES2 (µ = ' + str(ESmean)+ ' )'
fig.add_trace(go.Scatter(x=timesES, y=bpmES[1,:], name=name, mode='lines+markers'))
ESmean = np.around(np.mean(bpmES[2,:]),1)
name = 'E3 (µ = ' + str(ESmean)+ ' )'
fig.add_trace(go.Scatter(x=timesES, y=bpmES[2,:], name=name, mode='lines+markers'))
fig.update_layout(xaxis_title='Times (sec)', yaxis_title='BPM', showlegend=True)
fig.show()
def bpm_diff(bpmES, bpmGT, timesES=None, timesGT=None):
n,m = bpmES.shape # n = num channels, m = bpm length
if (timesES is None) or (timesGT is None):
timesES = np.arange(m)
timesGT = timesES
diff = np.zeros((n,m))
for j in range(m):
t = timesES[j]
i = np.argmin(np.abs(t-timesGT))
for c in range(n):
diff[c,j] = bpmGT[i]-bpmES[c,j]
return diff
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