"""
Quality-control table visualization.
"""
import pandas as pd
from bsrn.constants import WONG_PALETTE
from plotnine import (
ggplot, aes, geom_tile, geom_text,
theme, element_text, element_blank,
labs, scale_x_discrete, scale_y_discrete,
scale_fill_manual, theme_minimal,
)
[docs]
def plot_table(daily_stats, title=None, output_file=None):
"""
Plot QC statistics in a table-like heatmap format.
Parameters
----------
daily_stats : pd.DataFrame
Daily QC statistics calculated by `get_daily_stats`.
title : str, optional
Plot title. If None (default), no title is drawn.
output_file : str, optional
Path to save the plot (e.g., 'table.png').
Returns
-------
p : ggplot
The generated quality audit figure.
"""
df = daily_stats.copy()
# Format date as day-of-month for the Y axis
df['Day'] = [str(d.day) for d in df.index]
cols_to_plot = {
'SD_MAX': 'SD MAX',
'SD_ACT': 'SD ACT',
'SD_REL': 'SD REL',
'GHI_PPL': 'GHI PPL',
'GHI_ERL': 'GHI ERL',
'DHI_PPL': 'DHI PPL',
'DHI_ERL': 'DHI ERL',
'BNI_PPL': 'BNI PPL',
'BNI_ERL': 'BNI ERL',
'LWD_PPL': 'LWD PPL',
'LWD_ERL': 'LWD ERL',
'CMP_CLO': 'CLOSURE',
'CMP_DIF': 'DIF RATIO',
'CMP_K': 'K-INDEX',
'TRACKER': 'TRACKER'
}
available_cols = [c for c in cols_to_plot.keys() if c in df.columns]
plot_df = df[['Day'] + available_cols].copy()
melted = plot_df.melt(id_vars=['Day'], var_name='Metric', value_name='Value')
melted['Metric_Name'] = melted['Metric'].map(cols_to_plot)
order = [cols_to_plot[c] for c in available_cols]
melted['Metric_Name'] = pd.Categorical(melted['Metric_Name'], categories=order, ordered=True)
days_order = sorted([int(d) for d in melted['Day'].unique()], reverse=True)
melted['Day'] = pd.Categorical(melted['Day'], categories=[str(d) for d in days_order], ordered=True)
def get_category(row):
val = row['Value']
met = row['Metric']
if val == 0:
return 'Pass'
if 'SD' in str(met):
return 'Stats'
if 'PPL' in str(met):
return 'L1_Fail'
if 'ERL' in str(met):
return 'L2_Fail'
return 'L3_Fail'
melted['Category'] = melted.apply(get_category, axis=1)
# Apply Wong Palette
# White for Pass, others mapped logically
# Pass: white, Stats: WONG[1] (Sky Blue), L1: WONG[4] (Vermillion),
# L2: WONG[0] (Orange), L3: WONG[5] (Yellow)
fill_colors = {
'Pass': '#FFFFFF',
'Stats': WONG_PALETTE[1],
'L1_Fail': WONG_PALETTE[4],
'L2_Fail': WONG_PALETTE[0],
'L3_Fail': WONG_PALETTE[5]
}
# Format labels
def format_val(row):
val = row['Value']
met = row['Metric']
if 'REL' in str(met):
return f"{val:.0f}%"
if 'SD' in str(met):
return f"{val:.1f}"
return f"{val:.0f}"
melted['Label'] = melted.apply(format_val, axis=1)
# Figure dimensions: width 160mm; height scales with row count
_font_pt = 9
width_inch = 160 / 25.4
height_inch = (len(days_order) * 0.15) + 1.2
_labs = {"x": "", "y": "Date"}
if title is not None:
_labs["title"] = title
p = (
ggplot(melted, aes(x='Metric_Name', y='Day', fill='Category')) +
geom_tile(color='#D0D0D0', size=0.3) +
geom_text(aes(label='Label'), size=_font_pt, family='Times New Roman') +
scale_fill_manual(values=fill_colors, guide=None) +
scale_x_discrete() +
labs(**_labs) +
theme_minimal() +
theme(
text=element_text(family='Times New Roman', size=_font_pt),
axis_text_x=element_text(rotation=45, hjust=0.5, size=_font_pt),
axis_text_y=element_text(size=_font_pt),
axis_title=element_text(size=_font_pt),
plot_title=element_text(size=_font_pt, margin={'b': 5}),
panel_grid=element_blank(),
figure_size=(width_inch, height_inch)
)
)
if output_file:
p.save(output_file, dpi=300)
return p
