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9253c67 fa58ff0 9253c67 fa58ff0 9253c67 fa58ff0 9253c67 637bd04 fa58ff0 637bd04 fa58ff0 637bd04 9253c67 637bd04 9253c67 fa58ff0 9253c67 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | import pandas as pd
import streamlit as st
from config.config import CRRA_COLORS, CRRA_FONT, PLOTLY_TEMPLATE
def apply_chart_style(fig, height=None):
"""Apply the standard CRRA theme (font, colours, margins) to a Plotly figure.
Args:
fig: Plotly figure object.
height (int | None): optional fixed height in pixels.
Returns:
fig: the same figure with updated layout.
"""
fig.update_layout(
template=PLOTLY_TEMPLATE,
font=dict(family=CRRA_FONT, size=12, color=CRRA_COLORS["black"]),
margin=dict(t=40, b=40, l=40, r=20),
legend=dict(
orientation="v",
yanchor="middle", y=0.5,
xanchor="left", x=1.02,
),
)
if height:
fig.update_layout(height=height)
return fig
def register_chart_data(name: str, df: pd.DataFrame):
"""Store a chart's underlying DataFrame in the session-level registry for bulk export.
Args:
name (str): unique chart label used as the dict key and export sheet name.
df (pd.DataFrame): data behind the chart.
Returns:
None
"""
st.session_state.setdefault("chart_data_registry", {})
st.session_state["chart_data_registry"][name] = df.copy()
def show_bar():
"""Render the CRRA four-colour decorative bar below page headings.
Returns:
None
"""
st.markdown(
"""
<div class="et_pb_module">
<div style="display:flex;width:100%;height:12px;margin:0 auto 0 0;">
<span style="flex:1;background:#FF5F4D;"></span>
<span style="flex:1;background:#A0A0A0;"></span>
<span style="flex:1;background:#D9E210;"></span>
<span style="flex:1;background:#55C95B;"></span>
</div>
</div>
""",
unsafe_allow_html=True,
)
def smart_display_format(val):
"""Format a numeric value showing all significant digits, no rounding.
Uses repr() which gives the shortest decimal string that round-trips
exactly to the same float — avoids floating-point noise like
462961.099999999976717 when the value is actually 462961.1.
Falls back to fixed-decimal notation when repr() produces scientific
notation (very small numbers such as 1e-11).
Args:
val (float): numeric value to format.
Returns:
str: formatted string, or "" for near-zero values.
"""
if abs(val) < 1e-14:
return ""
s = repr(val)
if "e" not in s.lower():
return s
# Scientific notation → convert to fixed decimal (e.g. 1e-11 → 0.00000000001)
return f"{val:.15f}".rstrip("0").rstrip(".")
def format_efficiency_summary(df: pd.DataFrame, df_default: pd.DataFrame):
"""Build a styled Pandas Styler for the resource-efficiency coefficient summary table.
Cells whose value differs from the default median are highlighted in red.
Args:
df (pd.DataFrame): current coefficient values (methods × resources).
df_default (pd.DataFrame): default median values for the same shape.
Returns:
pandas.io.formats.style.Styler: styled dataframe ready for st.dataframe().
"""
df_numeric = df.copy()
df_display = df_numeric.map(smart_display_format)
def highlight_changes(data):
styled = pd.DataFrame('', index=data.index, columns=data.columns)
for m in data.index:
for r in data.columns:
try:
current = df_numeric.at[m, r]
default = df_default.at[m, r]
if abs(current - default) > 1e-6:
styled.at[m, r] = 'background-color: #ff5f4d'
except:
continue
return styled
return (
df_display
.style
.apply(highlight_changes, axis=None)
) |