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refactored plotting function #175

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Merged
RemDelaporteMathurin merged 1 commit into from
Sep 8, 2025
Merged

refactored plotting function #175

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169 changes: 95 additions & 74 deletions src/backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -335,6 +335,77 @@ def make_csv_payload(scopes):
return csv_payload


def make_plot(simulation):
scopes = [block for block in simulation.blocks if isinstance(block, Scope)]
spectra = [block for block in simulation.blocks if isinstance(block, Spectrum)]
print(f"Found {len(scopes)} scopes and {len(spectra)} spectra")

# FIXME right now only the scopes are converted to CSV
# extra work is needed since spectra and scopes don't share the same x axis
csv_payload = make_csv_payload(scopes)

# Share x only if there are only scopes or only spectra
shared_x = len(scopes) * len(spectra) == 0
n_rows = len(scopes) + len(spectra)

if n_rows == 0:
# No scopes or spectra to plot
return jsonify(
{
"success": True,
"plot": "{}",
"html": "<p>No scopes or spectra to display</p>",
"csv_data": csv_payload,
"message": "Pathsim simulation completed successfully",
}
)

absolute_vertical_spacing = 0.05
relative_vertical_spacing = absolute_vertical_spacing / n_rows
fig = make_subplots(
rows=n_rows,
cols=1,
shared_xaxes=shared_x,
subplot_titles=[scope.label for scope in scopes]
+ [spec.label for spec in spectra],
vertical_spacing=relative_vertical_spacing,
)

# make scope plots
for i, scope in enumerate(scopes):
sim_time, data = scope.read()

for p, d in enumerate(data):
lb = scope.labels[p] if p < len(scope.labels) else f"port {p}"
if isinstance(scope, Spectrum):
d = abs(d)
fig.add_trace(
go.Scatter(x=sim_time, y=d, mode="lines", name=lb),
row=i + 1,
col=1,
)

fig.update_xaxes(title_text="Time", row=len(scopes), col=1)

# make spectrum plots
for i, spec in enumerate(spectra):
freq, data = spec.read()

for p, d in enumerate(data):
lb = spec.labels[p] if p < len(spec.labels) else f"port {p}"
d = abs(d)
fig.add_trace(
go.Scatter(x=freq, y=d, mode="lines", name=lb),
row=len(scopes) + i + 1,
col=1,
)
fig.update_xaxes(title_text="Frequency", row=len(scopes) + i + 1, col=1)

fig.update_layout(height=500 * (len(scopes) + len(spectra)), hovermode="x unified")

return fig, csv_payload


# Function to convert graph to pathsim and run simulation
@app.route("/run-pathsim", methods=["POST"])
def run_pathsim():
Expand All @@ -354,93 +425,43 @@ def run_pathsim():
my_simulation.run(duration)

# Generate the plot
scopes = [block for block in my_simulation.blocks if isinstance(block, Scope)]
spectra = [
block for block in my_simulation.blocks if isinstance(block, Spectrum)
]

# FIXME right now only the scopes are converted to CSV
# extra work is needed since spectra and scopes don't share the same x axis
csv_payload = make_csv_payload(scopes)
try:
fig, csv_payload = make_plot(my_simulation)
print("Created plot figure")

# Share x only if there are only scopes or only spectra
shared_x = len(scopes) * len(spectra) == 0
n_rows = len(scopes) + len(spectra)
# Convert plot to JSON
try:
plot_data = plotly_json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
plot_html = fig.to_html()
print("Converted plot to JSON and HTML")
except Exception as plot_error:
print(f"Error converting plot to JSON: {str(plot_error)}")
return jsonify(
{
"success": False,
"error": f"Plot generation error: {str(plot_error)}",
}
), 500

if n_rows == 0:
# No scopes or spectra to plot
return jsonify(
{
"success": True,
"plot": "{}",
"html": "<p>No scopes or spectra to display</p>",
"plot": plot_data,
"html": plot_html,
"csv_data": csv_payload,
"message": "Pathsim simulation completed successfully",
}
)

absolute_vertical_spacing = 0.05
relative_vertical_spacing = absolute_vertical_spacing / n_rows
fig = make_subplots(
rows=n_rows,
cols=1,
shared_xaxes=shared_x,
subplot_titles=[scope.label for scope in scopes]
+ [spec.label for spec in spectra],
vertical_spacing=relative_vertical_spacing,
)

# make scope plots
for i, scope in enumerate(scopes):
sim_time, data = scope.read()

for p, d in enumerate(data):
lb = scope.labels[p] if p < len(scope.labels) else f"port {p}"
if isinstance(scope, Spectrum):
d = abs(d)
fig.add_trace(
go.Scatter(x=sim_time, y=d, mode="lines", name=lb), row=i + 1, col=1
)

fig.update_xaxes(title_text="Time", row=len(scopes), col=1)

# make spectrum plots
for i, spec in enumerate(spectra):
freq, data = spec.read()

for p, d in enumerate(data):
lb = spec.labels[p] if p < len(spec.labels) else f"port {p}"
d = abs(d)
fig.add_trace(
go.Scatter(x=freq, y=d, mode="lines", name=lb),
row=len(scopes) + i + 1,
col=1,
)
fig.update_xaxes(title_text="Frequency", row=len(scopes) + i + 1, col=1)

fig.update_layout(
height=500 * (len(scopes) + len(spectra)), hovermode="x unified"
)

# Convert plot to JSON
try:
plot_data = plotly_json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
plot_html = fig.to_html()
except Exception as plot_error:
except Exception as plot_creation_error:
print(f"Error during plot creation: {str(plot_creation_error)}")
return jsonify(
{"success": False, "error": f"Plot generation error: {str(plot_error)}"}
{
"success": False,
"error": f"Plot creation error: {str(plot_creation_error)}",
}
), 500

return jsonify(
{
"success": True,
"plot": plot_data,
"html": plot_html,
"csv_data": csv_payload,
"message": "Pathsim simulation completed successfully",
}
)

except Exception as e:
# Log the full error for debugging
import traceback
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