phil
ec71b6ed15
Allow sqlmodel queries, with relations Remode join_with mechanisms coming from gino Handlew ith_only_columns in get_df and get_gdf Implement feature-info
136 lines
6.1 KiB
Python
136 lines
6.1 KiB
Python
from typing import Any, Dict, Tuple, Type, ClassVar
|
|
import logging
|
|
|
|
from sqlmodel import Field, SQLModel, MetaData, JSON, TEXT, Relationship, Column, select
|
|
from pydantic import computed_field
|
|
import numpy as np
|
|
import pandas as pd
|
|
import geopandas as gpd
|
|
import shapely
|
|
from sqlalchemy.sql import sqltypes
|
|
from geoalchemy2.types import Geometry
|
|
|
|
from gisaf.database import BaseModel
|
|
|
|
pandas_cast_map = {
|
|
sqltypes.Integer: 'Int64',
|
|
sqltypes.Float: 'float64',
|
|
}
|
|
|
|
logger = logging.getLogger('model_base_base')
|
|
|
|
class Model(BaseModel):
|
|
"""
|
|
Base mixin class for models that can be converted to a Pandas dataframe with get_df
|
|
"""
|
|
# status: ClassVar[str] = 'E'
|
|
|
|
def __new__(cls, *args, **kwargs):
|
|
if not hasattr(cls, 'query'):
|
|
cls.query = select(cls)
|
|
return super().__new__(cls, *args, **kwargs)
|
|
|
|
class Meta:
|
|
filtered_columns_on_map: list[str] = []
|
|
|
|
@classmethod
|
|
def get_store_name(cls):
|
|
if hasattr(cls, '__table__'):
|
|
return cls.__table__.fullname
|
|
elif hasattr(cls, '__table_args__') and 'schema' in cls.__table_args__:
|
|
return f"{cls.__table_args__['schema']}.{cls.__tablename__}"
|
|
else:
|
|
return f'{cls.metadata.schema}.{cls.__tablename__}'
|
|
|
|
@classmethod
|
|
def get_table_name_prefix(cls):
|
|
return "{}_{}".format(cls.metadata.schema, cls.__tablename__)
|
|
|
|
# @classmethod
|
|
# async def get_df(cls, where=None,
|
|
# with_related=None, recursive=True,
|
|
# cast=True,
|
|
# with_only_columns=None,
|
|
# geom_as_ewkt=False,
|
|
# **kwargs):
|
|
# """
|
|
# Return a Pandas dataframe of all records
|
|
# Optional arguments:
|
|
# * an SQLAlchemy where clause
|
|
# * with_related: automatically get data from related columns, following the foreign keys in the model definitions
|
|
# * cast: automatically transform various data in their best python types (eg. with date, time...)
|
|
# * with_only_columns: fetch only these columns (list of column names)
|
|
# * geom_as_ewkt: convert geometry columns to EWKB (handy for eg. using upsert_df)
|
|
# :return:
|
|
# """
|
|
# breakpoint()
|
|
# if hasattr(cls, 'query'):
|
|
# query = cls.query
|
|
# else:
|
|
# query = select(cls)
|
|
|
|
# # if with_related is not False:
|
|
# # if with_related or getattr(cls, 'get_gdf_with_related', False):
|
|
# # joins = get_join_with(cls, recursive)
|
|
# # model_loader = cls.load(**joins)
|
|
# # query = _get_query_with_table_names(model_loader)
|
|
|
|
# if where is not None:
|
|
# query.append_whereclause(where)
|
|
|
|
# if with_only_columns:
|
|
# query = query.with_only_columns([getattr(cls, colname) for colname in with_only_columns])
|
|
|
|
# ## Got idea from https://github.com/MagicStack/asyncpg/issues/173.
|
|
# breakpoint()
|
|
# async with query.bind.raw_pool.acquire() as conn:
|
|
# ## Convert hstore fields to dict
|
|
# await conn.set_builtin_type_codec('hstore', codec_name='pg_contrib.hstore')
|
|
|
|
# compiled = query.compile()
|
|
# stmt = await conn.prepare(compiled.string)
|
|
# columns = [a.name for a in stmt.get_attributes()]
|
|
# data = await stmt.fetch(*[compiled.params.get(param) for param in compiled.positiontup])
|
|
# df = pd.DataFrame(data, columns=columns)
|
|
|
|
# ## Convert primary key columns to Int64:
|
|
# ## allows NaN, fixing type convertion to float with merge
|
|
# for pk in [c.name for c in cls.__table__.primary_key.columns]:
|
|
# if pk in df.columns and df[pk].dtype=='int64':
|
|
# df[pk] = df[pk].astype('Int64')
|
|
|
|
# if cast:
|
|
# ## Cast the type for known types (datetime, ...)
|
|
# for column_name in df.columns:
|
|
# col = getattr(query.columns, column_name, None)
|
|
# if col is None:
|
|
# logger.debug(f'Cannot get column {column_name} in query for model {cls.__name__}')
|
|
# continue
|
|
# column_type = getattr(query.columns, column_name).type
|
|
# ## XXX: Needs refinment, eg. nullable -> Int64 ...
|
|
# if column_type.__class__ in pandas_cast_map:
|
|
# df[column_name] = df[column_name].astype(pandas_cast_map[column_type.__class__])
|
|
# elif isinstance(column_type, (sqltypes.Date, sqltypes.DateTime)):
|
|
# ## Dates, times
|
|
# df[column_name] = pd.to_datetime(df[column_name])
|
|
# #elif isinstance(column_type, (sqltypes.Integer, sqltypes.Float)):
|
|
# # ## Numeric
|
|
# # df[column_name] = pd.to_numeric(df[column_name], errors='coerce')
|
|
# ## XXX: keeping this note about that is about "char" SQL type, but the fix of #9694 makes it unnessary
|
|
# #elif isinstance(column_type, sqltypes.CHAR) or (isinstance(column_type, sqltypes.String) and column_type.length == 1):
|
|
# # ## Workaround for bytes being used for string of length 1 (not sure - why???)
|
|
# # df[column_name] = df[column_name].str.decode('utf-8')
|
|
|
|
# ## Rename the columns, removing the schema_table prefix for the columns in that model
|
|
# prefix = cls.get_table_name_prefix()
|
|
# prefix_length = len(prefix) + 1
|
|
# rename_map = {colname: colname[prefix_length:] for colname in df.columns if colname.startswith(prefix)}
|
|
# df.rename(columns=rename_map, inplace=True)
|
|
|
|
# ## Eventually convert geometry columns to EWKB
|
|
# if geom_as_ewkt:
|
|
# geometry_columns = [col.name for col in cls.__table__.columns if isinstance(col.type, Geometry)]
|
|
# for column in geometry_columns:
|
|
# df[column] = shapely.to_wkb(shapely.from_wkb(df.geom), hex=True, include_srid=True)
|
|
|
|
# return df
|