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99f834c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | """
Abstract database connector interface and SchemaMapper.
Every database backend (SQLite, PostgreSQL, CSV) implements DatabaseConnector.
SchemaMapper translates arbitrary column names to the mRNASequence model fields.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
import pandas as pd
from core.models.sequence import mRNASequence
# Fields in mRNASequence that can be mapped from a database
SEQUENCE_FIELDS = {
"name",
"five_prime_utr",
"kozak",
"cds",
"three_prime_utr",
"poly_a",
"full_mrna",
}
@dataclass
class ConnectionConfig:
"""Generic connection configuration (fields vary by backend)."""
backend: str # "sqlite", "postgres", "csv", "excel"
display_name: str # User-facing label for the connection
params: Dict[str, Any] = field(default_factory=dict)
# e.g. sqlite: {"path": "/data/seqs.db"}
# e.g. postgres: {"host": "...", "port": 5432, "dbname": "...", "user": "...", "password": "..."}
# e.g. csv: {"path": "/data/seqs.csv"}
class DatabaseConnector(ABC):
"""Abstract database connector. One instance per active connection."""
def __init__(self, config: ConnectionConfig) -> None:
self.config = config
self._connected = False
@abstractmethod
def connect(self) -> None:
"""Open the connection. Raises ConnectionError on failure."""
...
@abstractmethod
def disconnect(self) -> None:
"""Close the connection."""
...
@abstractmethod
def list_tables(self) -> List[str]:
"""Return available table / sheet names."""
...
@abstractmethod
def get_records(
self,
table: str,
query: Optional[str] = None,
limit: Optional[int] = None,
) -> pd.DataFrame:
"""
Fetch records from a table.
Parameters
----------
table : str
Table name (from list_tables).
query : str, optional
Backend-specific filter string (SQL WHERE clause for SQL backends,
pandas query string for file backends).
limit : int, optional
Max rows to return.
"""
...
@abstractmethod
def get_columns(self, table: str) -> List[str]:
"""Return column names for a table."""
...
@property
def is_connected(self) -> bool:
return self._connected
@property
def name(self) -> str:
return self.config.display_name
def __repr__(self) -> str:
status = "connected" if self._connected else "disconnected"
return f"{self.__class__.__name__}({self.name!r}, {status})"
# ── Schema Mapper ────────────────────────────────────────────────────────────
@dataclass
class FieldMapping:
"""
Describes how one database column maps to a mRNASequence field.
source_column : str
Column name in the database.
target_field : str
Field name in mRNASequence. Must be in SEQUENCE_FIELDS.
transform : callable, optional
Optional transform applied to the raw value before assignment.
E.g. str.upper, lambda x: x.replace(" ", "")
"""
source_column: str
target_field: str
transform: Optional[Any] = None # callable or None
def __post_init__(self) -> None:
if self.target_field not in SEQUENCE_FIELDS:
raise ValueError(
f"'{self.target_field}' is not a valid mRNASequence field. "
f"Valid fields: {sorted(SEQUENCE_FIELDS)}"
)
class SchemaMapper:
"""
Maps a DataFrame (from any DatabaseConnector) to a list of mRNASequence
objects using a user-configured field mapping.
Example
-------
mapper = SchemaMapper([
FieldMapping("mrna_sequence", "full_mrna"),
FieldMapping("gene_name", "name"),
FieldMapping("utr5_sequence", "five_prime_utr", transform=str.upper),
])
sequences = mapper.map_dataframe(df, db_source="my_lims")
"""
def __init__(self, mappings: List[FieldMapping], db_source: str = "") -> None:
self.mappings = mappings
self.db_source = db_source
# Validate: exactly one mapping targeting 'name' must exist
name_targets = [m for m in mappings if m.target_field == "name"]
if not name_targets:
raise ValueError(
"SchemaMapper requires at least one FieldMapping targeting 'name'."
)
def map_row(self, row: Dict[str, Any]) -> mRNASequence:
"""Map a single row dict to an mRNASequence."""
kwargs: Dict[str, Any] = {
"source": "database",
"db_source": self.db_source,
"raw_metadata": dict(row),
}
for mapping in self.mappings:
value = row.get(mapping.source_column)
# Skip None and NaN values (pandas often returns NaN for SQL NULL)
if value is None or (isinstance(value, float) and pd.isna(value)):
continue
if mapping.transform is not None:
try:
value = mapping.transform(value)
except Exception:
pass
kwargs[mapping.target_field] = value
# name is required — fall back to first non-empty string value in the row
if "name" not in kwargs or not kwargs["name"]:
for v in row.values():
if isinstance(v, str) and v.strip():
kwargs["name"] = v.strip()[:80]
break
else:
kwargs["name"] = "unnamed"
return mRNASequence(**kwargs) # type: ignore[arg-type]
def map_dataframe(self, df: pd.DataFrame) -> List[mRNASequence]:
"""Map every row in df to an mRNASequence."""
return [self.map_row(row.to_dict()) for _, row in df.iterrows()]
@classmethod
def from_dict(cls, mapping_dict: Dict[str, str], db_source: str = "") -> "SchemaMapper":
"""
Convenience constructor from a plain {db_column: sequence_field} dict.
Example
-------
mapper = SchemaMapper.from_dict({
"gene_name": "name",
"mrna_seq": "full_mrna",
"utr": "five_prime_utr",
})
"""
mappings = [
FieldMapping(source_column=col, target_field=field_)
for col, field_ in mapping_dict.items()
]
return cls(mappings, db_source=db_source)
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