Spaces:
Running
Running
File size: 3,950 Bytes
4d886f4 34cacad 4d886f4 | 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 | """Human-readable labels for compact codes used in cached tables."""
from __future__ import annotations
import numpy as np
import pandas as pd
# Matches interpretation.predictions._get_modality_info letter codes (R/A/F order).
# Short table-friendly labels (no long parentheticals).
_MODALITY_LONG: dict[str, str] = {
"RAF": "RNA + ATAC + Flux",
"RA": "RNA + ATAC",
"RF": "RNA + Flux",
"AF": "ATAC + Flux",
"R": "RNA only",
"A": "ATAC only",
"F": "Flux only",
"None": "No modality data",
"none": "No modality data",
"nan": "No modality data",
}
# Rename row fields in inspector tables for display.
_FIELD_DISPLAY: dict[str, str] = {
"label": "CellTag-Multi label",
}
# Latent explorer: table headers and key–value inspector (exclude non-meaningful / internal cols).
LATENT_TABLE_RENAME: dict[str, str] = {
"label": "CellTag-Multi label",
"predicted_class": "Predicted fate",
"predicted_value": "Prediction score",
"correct": "Prediction correct",
"pct": "Dominant fate (%)",
"modality_label": "Available modalities",
"dataset_idx": "Dataset index",
"batch_no": "Batch",
"fold": "Cross Validation fold",
"clone_id": "Clone ID",
"clone_size": "Clone size",
"cell_type": "Cell type",
}
LATENT_DROP_FROM_TABLES: frozenset[str] = frozenset({"umap_x", "umap_y", "modality", "pct_decile"})
_NAME_MAP = {**_FIELD_DISPLAY, **LATENT_TABLE_RENAME}
def _format_scalar(v) -> str:
if v is None:
return ""
if isinstance(v, bool):
return "Yes" if v else "No"
try:
if pd.isna(v):
return ""
except (ValueError, TypeError):
pass
if isinstance(v, (float, np.floating)) and np.isnan(v):
return ""
return str(v)
def _field_label(name: str, *, fallback_field_display: bool) -> str:
k = str(name)
if fallback_field_display:
return _NAME_MAP.get(k, _FIELD_DISPLAY.get(k, k))
return _NAME_MAP.get(k, k)
def expand_modality(code) -> str:
"""Map R/A/F codes (e.g. RAF, RA) to full names."""
if code is None:
return _MODALITY_LONG["None"]
try:
if pd.isna(code):
return _MODALITY_LONG["None"]
except (ValueError, TypeError):
pass
if isinstance(code, (float, np.floating)) and np.isnan(code):
return _MODALITY_LONG["None"]
key = str(code).strip()
if not key or key.lower() == "nan":
return _MODALITY_LONG["None"]
return _MODALITY_LONG.get(key, key)
def annotate_modality_column(df, code_col: str = "modality", label_col: str = "modality_label"):
"""Add human-readable modality column; returns a copy."""
out = df.copy()
out[label_col] = out[code_col].map(expand_modality)
return out
def prepare_latent_display_dataframe(df: pd.DataFrame) -> pd.DataFrame:
"""Drop UMAP / internal columns and rename headers for Selected-points style tables."""
drop = [c for c in df.columns if c in LATENT_DROP_FROM_TABLES or str(c).startswith("umap_")]
out = df.drop(columns=drop, errors="ignore")
return out.rename(columns=LATENT_TABLE_RENAME)
def latent_inspector_key_value(series: pd.Series) -> pd.DataFrame:
"""Key–value inspector row: human names, no UMAP coordinates."""
s = series.drop(
labels=[c for c in series.index if c in LATENT_DROP_FROM_TABLES or str(c).startswith("umap_")],
errors="ignore",
)
idx = [_field_label(i, fallback_field_display=False) for i in s.index]
vals = [_format_scalar(v) for v in s.values]
return pd.DataFrame({"Field": idx, "Value": vals})
def dataframe_to_arrow_safe_kv(series: pd.Series) -> pd.DataFrame:
"""Two string columns for Streamlit/PyArrow (avoids mixed-type single column)."""
s = series.copy()
idx = [_field_label(i, fallback_field_display=True) for i in s.index]
vals = [_format_scalar(v) for v in s.values]
return pd.DataFrame({"field": idx, "value": vals})
|