APOG_public/acsmnode
0
0
Fork 0
mirror of https://gitea.psi.ch/APOG/acsmnode.git synced 2025-06-26 19:41:12 +02:00
Code Issues Packages Projects Releases Wiki Activity

update pipelines/steps/visualize_datatable_vars.py with interactive plots and refactor dependent jupyter nb accordingly

Browse Source
This commit is contained in:
florez_j
2025-05-13 11:32:46 +02:00
parent 5a54a22ebd
commit 37671a3685
2 changed files with 80 additions and 194 deletions
Download Patch File Download Diff File Expand all files Collapse all files

187
notebooks/demo_visualize_diagnostic_flags_from_hdf5_file.ipynb
View File

File diff suppressed because one or more lines are too long

87
pipelines/steps/visualize_datatable_vars.py
View File

@ -4,7 +4,7 @@ import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
def visualize_table_variables(data_file_path, dataset_name, flags_dataset_name, x_var, y_vars):
@ -83,15 +83,26 @@ def visualize_table_variables(data_file_path, dataset_name, flags_dataset_name,
#fig, ax = plt.subplots(len(y_vars), 1, figsize=(12, 5))
figs = [] # store each figure
for var_idx, var in enumerate(y_vars):
#y = dataset_df[var].to_numpy()
# Plot Flow Rate
fig = plt.figure(var_idx,figsize=(12, 2.5))
ax = plt.gca()
#ax = fig.get_axes()
ax.plot(dataset_df[x_var], dataset_df[var], label=var, alpha=0.8, color='tab:blue')
#fig = plt.figure(var_idx,figsize=(12, 2.5))
#ax = plt.gca()
#ax.plot(dataset_df[x_var], dataset_df[var], label=var, alpha=0.8, color='tab:blue')
fig = go.Figure()
# Main line plot
fig.add_trace(go.Scatter(
x=dataset_df[x_var],
y=dataset_df[var],
mode='lines',
name=var,
line=dict(color='blue'),
opacity=0.8
))
# Specify flag name associated with var name in y_vars. By construction, it is assumed the name satisfy the following sufix convention.
var_flag_name = f"flag_{var}"
@ -106,34 +117,70 @@ def visualize_table_variables(data_file_path, dataset_name, flags_dataset_name,
invalid_ends = np.diff(np.concatenate(([False], ind_invalid, [False]))).nonzero()[0][1::2]
# Fill invalid regions
t_base = dataset_df[x_var].to_numpy()
t_base = dataset_df[x_var] #.to_numpy()
y_min, y_max = dataset_df[var].min(), dataset_df[var].max()
max_idx = len(t_base) - 1 # maximum valid index
for start, end in zip(invalid_starts, invalid_ends):
if start >= end:
print(f"Warning: Skipping invalid interval — start ({start}) >= end ({end})")
continue
# Clip start and end to valid index range
continue # Clip start and end to valid index range
start = max(0, start)
end = min(end, max_idx)
ax.fill_betweenx([dataset_df[var].min(), dataset_df[var].max()], t_base[start], t_base[end],
color='red', alpha=0.3, label="Invalid Data" if start == invalid_starts[0] else "")
#ax.fill_betweenx([dataset_df[var].min(), dataset_df[var].max()], t_base[start], t_base[end],
# color='red', alpha=0.3, label="Invalid Data" if start == invalid_starts[0] else "")
# start = max(0, start)
fig.add_shape(
type="rect",
x0=t_base[start], x1=t_base[end],
y0=y_min, y1=y_max,
fillcolor="red",
opacity=0.3,
line_width=0,
layer="below"
)
# Add a dummy invisible trace just for the legend
fig.add_trace(go.Scatter(
x=[None], y=[None],
mode='markers',
marker=dict(size=10, color='red', opacity=0.3),
name='Invalid Region'
))
# Labels and Legends
ax.set_xlabel(x_var)
ax.set_ylabel(var)
ax.legend()
ax.grid(True)
#ax.set_xlabel(x_var)
#ax.set_ylabel(var)
#ax.legend()
#ax.grid(True)
#plt.tight_layout()
#plt.show()
return fig, ax
#return fig, ax
# Add layout
fig.update_layout(
title=f"{var} over {x_var}",
xaxis_title=x_var,
yaxis_title=var,
xaxis_range = [t_base.min(), t_base.max()],
showlegend=True,
height=300,
margin=dict(l=40, r=20, t=40, b=40),
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
)
fig.show()
figs.append(fig)
# Optionally return figs if needed
return figs