def _make_plot(self, counter, feat, predicted, truth):
try:
td = TrainData_NanoML() #contains all dicts
truths = td.createTruthDict(feat)
feats = td.createFeatureDict(feat, addxycomb=False)
data = {}
data.update(truths)
data.update(feats)
data['recHitLogEnergy'] = np.log(data['recHitEnergy'] + 1)
coords = predicted[self.use_prediction_idx]
if not coords.shape[-1] == 3:
print(
"plotGravNetCoordsDuringTraining only supports 3D coordinates"
) #2D and >3D TBI
return #not supported
data['coord A'] = coords[:, 0:1]
data['coord B'] = coords[:, 1:2]
data['coord C'] = coords[:, 2:3]
df = pd.DataFrame(np.concatenate([data[k] for k in data], axis=1),
columns=[k for k in data])
shuffle_truth_colors(df)
fig = px.scatter_3d(
df,
x="coord A",
y="coord B",
z="coord C",
color="truthHitAssignementIdx",
size="recHitLogEnergy",
symbol="recHitID",
#hover_data=[],
template='plotly_dark',
color_continuous_scale=px.colors.sequential.Rainbow)
fig.update_traces(marker=dict(line=dict(width=0)))
ccfile = self.outputfile + str(
self.keep_counter) + "_coords_" + str(
self.use_prediction_idx) + ".html"
fig.write_html(ccfile)
if self.publish is not None:
publish(ccfile, self.publish)
except Exception as e:
print(e)
raise e
def _make_plot(self, counter, feat, predicted,
truth): #all these are lists and also include row splits
try:
td = TrainData_NanoML() #contains all dicts
#row splits not needed
feats = td.createFeatureDict(feat, addxycomb=False)
backgather = predicted[self.use_backgather_idx]
truths = td.createTruthDict(feat)
data = {}
data.update(feats)
data.update(truths)
if len(backgather.shape) < 2:
backgather = np.expand_dims(backgather, axis=1)
data['recHitLogEnergy'] = np.log(data['recHitEnergy'] + 1)
data['hitBackGatherIdx'] = backgather
from globals import cluster_space as cs
removednoise = np.logical_and(data["recHitX"] == cs.noise_coord,
data["recHitY"] == cs.noise_coord)
removednoise = np.logical_and(data["recHitZ"] == cs.noise_coord,
removednoise)
#remove removed noise
for k in data.keys():
data[k] = data[k][not removednoise]
df = pd.DataFrame(np.concatenate([data[k] for k in data], axis=1),
columns=[k for k in data])
shuffle_truth_colors(df)
fig = px.scatter_3d(
df,
x="recHitX",
y="recHitZ",
z="recHitY",
color="truthHitAssignementIdx",
size="recHitLogEnergy",
symbol="recHitID",
#template='plotly_dark',
color_continuous_scale=px.colors.sequential.Rainbow)
fig.update_traces(marker=dict(line=dict(width=0)))
fig.write_html(self.outputfile + str(self.keep_counter) +
"_truth.html")
bgfile = self.outputfile + str(
self.keep_counter) + "_backgather.html"
#now the cluster indices
shuffle_truth_colors(df, "hitBackGatherIdx")
fig = px.scatter_3d(
df,
x="recHitX",
y="recHitZ",
z="recHitY",
color="hitBackGatherIdx",
size="recHitLogEnergy",
#template='plotly_dark',
color_continuous_scale=px.colors.sequential.Rainbow)
fig.update_traces(marker=dict(line=dict(width=0)))
fig.write_html(bgfile)
if self.publish is not None:
publish(bgfile, self.publish)
except Exception as e:
print(e)
raise e
def _make_plot(self, counter, feat, predicted, truth):
try:
'''
[pred_beta,
pred_ccoords,
pred_energy,
pred_pos,
pred_time,
pred_id
'''
td = TrainData_NanoML() #contains all dicts
#row splits not needed
feats = td.createFeatureDict(feat, addxycomb=False)
truths = td.createTruthDict(feat)
predBeta = predicted['pred_beta']
print('>>>> plotting cluster coordinates... average beta',
np.mean(predBeta), ' lowest beta ', np.min(predBeta),
'highest beta', np.max(predBeta))
#for later
predEnergy = predicted['pred_energy_corr_factor']
predX = predicted['pred_pos'][:, 0:1]
predY = predicted['pred_pos'][:, 1:2]
predT = predicted['pred_time']
predD = predicted['pred_dist']
data = {}
data.update(feats)
data.update(truths)
predCCoords = predicted['pred_ccoords']
data['recHitLogEnergy'] = np.log(data['recHitEnergy'] + 1)
data['predBeta'] = predBeta
data[
'predBeta+0.05'] = predBeta + 0.05 #so that the others don't disappear
data['predEnergy'] = predEnergy
data['predX'] = predX
data['predY'] = predY
data['predT'] = predT
data['predD'] = predD
data['(predBeta+0.05)**2'] = data['predBeta+0.05']**2
data['(thresh(predBeta)+0.05))**2'] = np.where(
predBeta > self.beta_threshold, data['(predBeta+0.05)**2'], 0.)
if not predCCoords.shape[-1] == 3:
self.projection_plot(data, predCCoords)
return
data['predCCoordsX'] = predCCoords[:, 0:1]
data['predCCoordsY'] = predCCoords[:, 1:2]
data['predCCoordsZ'] = predCCoords[:, 2:3]
from globals import cluster_space as cs
removednoise = np.logical_and(
data["predCCoordsX"] == cs.noise_coord,
data["predCCoordsY"] == cs.noise_coord)
removednoise = np.logical_and(
data["predCCoordsZ"] == cs.noise_coord, removednoise)
removednoise = np.where(removednoise[:, 0], False, True)
#remove removed noise
for k in data.keys():
data[k] = data[k][removednoise]
df = pd.DataFrame(np.concatenate([data[k] for k in data], axis=1),
columns=[k for k in data])
#fig = px.scatter_3d(df, x="recHitX", y="recHitZ", z="recHitY", color="truthHitAssignementIdx", size="recHitLogEnergy")
#fig.write_html(self.outputfile + str(self.keep_counter) + "_truth.html")
shuffle_truth_colors(df)
#now the cluster indices
hover_data = [
'predBeta', 'predD', 'predEnergy', 'truthHitAssignedEnergies',
'predT', 'truthHitAssignedT', 'predX', 'truthHitAssignedX',
'predY', 'truthHitAssignedY', 'truthHitAssignementIdx'
]
fig = px.scatter_3d(
df,
x="predCCoordsX",
y="predCCoordsY",
z="predCCoordsZ",
color="truthHitAssignementIdx",
size="recHitLogEnergy",
symbol="recHitID",
hover_data=hover_data,
template='plotly_dark',
color_continuous_scale=px.colors.sequential.Rainbow)
fig.update_traces(marker=dict(line=dict(width=0)))
ccfile = self.outputfile + str(self.keep_counter) + "_ccoords.html"
fig.write_html(ccfile)
if self.publish is not None:
publish(ccfile, self.publish)
fig = px.scatter_3d(
df,
x="predCCoordsX",
y="predCCoordsY",
z="predCCoordsZ",
color="truthHitAssignementIdx",
size="(predBeta+0.05)**2",
hover_data=hover_data,
symbol="recHitID",
template='plotly_dark',
color_continuous_scale=px.colors.sequential.Rainbow)
fig.update_traces(marker=dict(line=dict(width=0)))
ccfile = self.outputfile + str(
self.keep_counter) + "_ccoords_betasize.html"
fig.write_html(ccfile)
if self.publish is not None:
publish(ccfile, self.publish)
# thresholded
fig = px.scatter_3d(
df,
x="recHitX",
y="recHitZ",
z="recHitY",
color="truthHitAssignementIdx",
size="recHitLogEnergy",
symbol="recHitID",
hover_data=[
'predBeta', 'predEnergy', 'predX', 'predY',
'truthHitAssignementIdx', 'truthHitAssignedEnergies',
'truthHitAssignedX', 'truthHitAssignedY'
],
template='plotly_dark',
color_continuous_scale=px.colors.sequential.Rainbow)
fig.update_traces(marker=dict(line=dict(width=0)))
ccfile = self.outputfile + str(self.keep_counter) + "_truth.html"
fig.write_html(ccfile)
if self.publish is not None:
publish(ccfile, self.publish)
except Exception as e:
print(e)
raise e