def plot_heatmapSingle(probabilityArray, fsds, skip, figsize = (5,2.5), threshold = 0.5, smoothing = 20, vmin = 0.1, vmax = 1.1, cmapName = "Blues"):
fig, ax = plt.subplots(1,1, figsize=figsize, dpi =300)
windows, nchan = probabilityArray.shape
w = int(smoothing*fsds/skip)
mvgAvg = np.zeros(shape = (windows - w + 1, nchan))
for c in range(nchan):
mvgAvg[:,c] = echobase.movingaverage(probabilityArray[:,c], w)
cmap = plt.cm.get_cmap(cmapName)
sns.heatmap( mvgAvg.T , cmap = cmap , ax = ax, cbar = False, xticklabels = False, yticklabels = False, vmin = vmin, vmax = vmax)
def prob_threshold_moving_avg(prob_array, fsds, skip, threshold = 0.9, smoothing = 20):
nchan = prob_array.shape[1]
w = int(smoothing*fsds/skip)
probability_arr_movingAvg = np.zeros(shape = (windows - w + 1, nchan))
for c in range(nchan):
probability_arr_movingAvg[:,c] = echobase.movingaverage(prob_array[:,c], w)
probability_arr_threshold = copy.deepcopy(probability_arr_movingAvg)
probability_arr_threshold[probability_arr_threshold > threshold] = 1
probability_arr_threshold[probability_arr_threshold <= threshold] = 0
return probability_arr_movingAvg, probability_arr_threshold
def plot_probheatmaps(probabilityArray, fsds, skip, threshold = 0.5, smoothing = 20, channel1 = 0, channel2 = 1, vmin = 0, vmax = 1, center=0.5):
fig, ax = plt.subplots(3,2, figsize=(10,10), dpi =300)
windows, nchan = probabilityArray.shape
thresholded = copy.deepcopy(probabilityArray)
thresholded[thresholded>threshold] = 1; thresholded[thresholded<=threshold] = 0
w = int(smoothing*fsds/skip)
mvgAvg = np.zeros(shape = (windows - w + 1, nchan))
for c in range(nchan):
mvgAvg[:,c] = echobase.movingaverage(probabilityArray[:,c], w)
mvgAvgThreshold = copy.deepcopy(mvgAvg)
mvgAvgThreshold[mvgAvgThreshold>threshold] = 1
mvgAvgThreshold[mvgAvgThreshold<=threshold] = 0
sns.lineplot( x = range(windows), y= probabilityArray[:,channel1], ci=None, ax = ax[0,0])
sns.lineplot( x = range(windows), y= probabilityArray[:,channel2], ci=None, ax = ax[0,1])
sns.heatmap( probabilityArray.T , ax = ax[1,0], vmin = vmin, vmax = vmax, center=center)
sns.heatmap( thresholded.T, ax = ax[1,1] )
sns.heatmap( mvgAvg.T , ax = ax[2,0] , vmin = vmin, vmax = vmax, center=center)
sns.heatmap( mvgAvgThreshold.T, ax = ax[2,1] )
def plot_singleProbability(probabilityArray, fsds, skip, channel=1, startInd = 0, stopInd = None, smoothing = 20, vmin = -0.2, vmax = 1.2):
windows, nchan = probabilityArray.shape
if stopInd == None:
stopInd = windows
smoothing = smoothing
w = int(smoothing*fsds/skip)
mvgAvg = np.zeros(shape = (windows - w + 1, nchan))
for c in range(nchan):
mvgAvg[:,c] = echobase.movingaverage(probabilityArray[:,c], w)
y= mvgAvg[startInd:stopInd,channel]
x = range(len(y))
y2 = -0.05
fig, ax = plt.subplots(1,1, figsize=(10,2), dpi =300)
sns.lineplot( x = x, y= y, ci=None, ax = ax, alpha = 0)
interpolate = interp1d(np.array(x),np.array(x))
xnew = interpolate(np.arange( 0, len(x)-1, 1 ))
interpolate = interp1d(np.array(x),y)
ynew = interpolate(np.arange( 0, len(x)-1, 1 ))
Z = np.linspace(0, 1, len(xnew))
normalize = colors.Normalize(vmin=vmin, vmax=vmax)
cmap = plt.cm.get_cmap('Blues')
df = pd.DataFrame({'x': xnew, 'y1': ynew, 'z': Z})
xcolor = df['x'].values
y1color = df['y1'].values
zcolor = df['z'].values
for ii in range(len(df['x'].values)-1):
plt.fill_between( x = [ xcolor[ii] , xcolor[(ii+1)]] , y1 = [ y1color[ii], y1color[ii+1] ], y2=y2, color=cmap(normalize(zcolor[ii])) )
ax.set(yticks=[])
ax.set(xticks=[])
ax.set_ylim([y2,1])
sns.despine(bottom=True, left=True)
for ch in range(len(c)):
print(ch)
ch_pred = data_scalerDSPlot_X[:,:,ch].reshape(windows, time_step, 1 )
probWN[:,ch] = modelWN.predict(ch_pred, verbose=1)[:,1]
probCNN[:,ch] = modelCNN.predict(ch_pred, verbose=1)[:,1]
probLSTM[:,ch] = modelLSTM.predict(ch_pred, verbose=1)[:,1]
smoothing = 20
w = int(smoothing*fsds/skip)
mvgAvg = np.zeros(shape = (windows - w + 1, len(c)))
for ch in range(len(c)):
mvgAvg[:,ch] = echobase.movingaverage(probWN[:,ch], w)
#Calculate Raw Features
#linelength, linelengthNorm = echobase.lineLengthOfArray(data_scalerDSPlot_X)
#%% generate example EEG
fnameFig = join(pathFigureOverview, "exampleEEGspread.png")
DataJson.plot_eeg(data_scaler[int(ictalStartIndex-1*fs_plot):int(ictalStopIndex+2*fs_plot),c], fs_plot, nchan = None, dpi = 300, aspect=30, height=0.5, hspace=-0.87, lw = 0.8, savefig = False, pathFig = fnameFig)
#%making pattern
def plot_probheatmaps(probabilityArray, fsds, skip, threshold = 0.5, smoothing = 20, channel1 = 0, channel2 = 1, vmin = 0, vmax = 1, center=0.5, title = None, title_channel1 = None, title_channel2 = None, verticalline = 1800, channel_names = ""):
fig, ax = plt.subplots(3,2, figsize=(16,16), dpi =300)
#printing channel names
channel_names_string = " ".join(channel_names)
names_len = len(channel_names_string)
max_char = 160 #max characters on single line
text_string = []
new_string = copy.deepcopy(channel_names_string)
s=0
while s < names_len:
spaces = np.array([i for i in range(len(new_string)) if new_string.startswith(" ", i)])
where_spaces_are_less_than_max = np.where(spaces < max_char)[0]
loc = spaces[where_spaces_are_less_than_max[-1]]
text_string.append( new_string[0:loc] )
s = s+loc+1
new_string = new_string[loc+1:]
if s+max_char >names_len:
text_string.append( new_string )
s = s + max_char
text = "\n".join(text_string)
fig.text(0.1,0, text, size = 9, wrap=True, ha='left', va = "bottom")
windows, nchan = probabilityArray.shape
thresholded = copy.deepcopy(probabilityArray)
thresholded[thresholded>threshold] = 1; thresholded[thresholded<=threshold] = 0
w = int(smoothing*fsds/skip)
mvgAvg = np.zeros(shape = (windows - w + 1, nchan))
for c in range(nchan):
mvgAvg[:,c] = echobase.movingaverage(probabilityArray[:,c], w)
mvgAvgThreshold = copy.deepcopy(mvgAvg)
mvgAvgThreshold[mvgAvgThreshold>threshold] = 1
mvgAvgThreshold[mvgAvgThreshold<=threshold] = 0
#plot non-smoothed
#sns.lineplot( x = range(windows), y= probabilityArray[:,channel1], ci=None, ax = ax[0,0])
#sns.lineplot( x = range(windows), y= probabilityArray[:,channel2], ci=None, ax = ax[0,1])
#plot smoothed
nan_add = windows - mvgAvg.shape[0]
lineplot1 = np.concatenate([ mvgAvg[:,channel1], np.repeat(np.nan, nan_add)])
lineplot2 = np.concatenate([ mvgAvg[:,channel2], np.repeat(np.nan, nan_add)])
np.array(range(windows)).shape[0]
sns.lineplot( x = range(windows), y= lineplot1, ci=None, ax = ax[0,0])
sns.lineplot( x = range(windows), y= lineplot2, ci=None, ax = ax[0,1])
ax[0,0].axhline(y=threshold, color="#562686", linestyle='-', lw = 3)
ax[0,1].axhline(y=threshold, color="#562686", linestyle='-', lw = 3)
ax[0,0].axvline(x=verticalline, c = "#aa2222", lw = 3 )
ax[0,1].axvline(x=verticalline, c = "#aa2222", lw = 3 )
sns.heatmap( probabilityArray.T , ax = ax[1,0], vmin = vmin, vmax = vmax, center=center)
sns.heatmap( thresholded.T, ax = ax[1,1] )
sns.heatmap( mvgAvg.T , ax = ax[2,0] , vmin = vmin, vmax = vmax, center=center)
sns.heatmap( mvgAvgThreshold.T, ax = ax[2,1] )
if title == None:
title = "Seizure Probabilities"
if title_channel1 == None:
title_channel1 = 'Channel 0'
if title_channel2 == None:
title_channel2 = 'Channel 1'
fig.suptitle(title, fontsize=28, y= 0.93)
ax[0,0].set_xlabel("time (0.1 sec)")
ax[0,1].set_xlabel("time (0.1 sec)")
ax[0,0].set_ylabel("seizure probability")
ax[0,0].title.set_text(title_channel1)
ax[0,1].title.set_text(title_channel2)
ax[1,0].set_ylabel("Channel #")
ax[2,0].set_ylabel("Channel #")
ax[2,0].set_xlabel("time (0.1 sec)")
ax[2,1].set_xlabel("time (0.1 sec)")
ax[1,0].set_xticklabels(labels = ax[2,0].get_xticklabels(), fontsize = 7)
ax[1,1].set_xticklabels(labels = ax[2,0].get_xticklabels(), fontsize = 7)
ax[2,0].set_xticklabels(labels = ax[2,0].get_xticklabels(), fontsize = 7)
ax[2,1].set_xticklabels(labels = ax[2,0].get_xticklabels(), fontsize = 7)
SMOOTHING = 20 #in seconds
lineLength_arr_threshold = copy.deepcopy(lineLength_arr)
lineLength_arr_threshold[lineLength_arr_threshold>THRESHOLD_LL] = 1
lineLength_arr_threshold[lineLength_arr_threshold<=THRESHOLD_LL] = 0
sns.heatmap( lineLength_arr_threshold.T, ax = ax[1,1] )
w = int(SMOOTHING*fsds/skip)
lineLength_arr_movingAvg = np.zeros(shape = (windows - w + 1, nchan))
for c in range(nchan):
lineLength_arr_movingAvg[:,c] = echobase.movingaverage(lineLength_arr[:,c], w)
sns.heatmap( lineLength_arr_movingAvg.T , ax = ax[2,0] )
lineLength_arr_movingAvg_threshold = copy.deepcopy(lineLength_arr_movingAvg)
lineLength_arr_movingAvg_threshold[lineLength_arr_movingAvg_threshold>THRESHOLD_LL] = 1
lineLength_arr_movingAvg_threshold[lineLength_arr_movingAvg_threshold<=THRESHOLD_LL] = 0
sns.heatmap( lineLength_arr_movingAvg_threshold.T, ax = ax[2,1] )
#sns.heatmap(SC, square=True)
